Essay On Fuzzy Logic

Essay On Fuzzy Logic
1.Introduction
Fuzzy system has been applied to various type of application or system. Fuzzy system is an
approach of the computational intelligence use a collection of fuzzy functions and rules to reason
about a data. Its use any Fuzzy Logic based system which use Fuzzy Logic as basic for
knowledgeable representation using different forms of knowledge. The main function of fuzzy logic
technology is it ability of propose an approximate solvent to an imprecisely formulated problem.
Fuzzy Logic in other meaning can be said as a procedure paradigm that is based on how human was
thinking. What can be said that fuzzy logic is closer to human reasoning than the classical logic.
Where then it attempts to precisely formulate and exactly solve a mathematical ... Show more
content on Helpwriting.net ...
There are plenty of the advantage in washing machine that use fuzzy logic such as performance,
productivity, simplicity, and it is less cost. The sensors continually monitor varying conditions inside
the machine and accordingly adjust operations for the best wash result. The example of brand that
use fuzzy logic in their product in washing machine is from Samsung WA80K8S. 2.2.1 Features
a)The washing machine feature 'OneTouch control'. It is equipped with energy saving features, that
is consume less power and are worth paying extra for if the user washes full loads more than three
times a week.
b)Practically dry straight from the washer. It will keep the clothes in pristine condition. It also will
minimises the moisture content in clothes.
c)Rescue clothes from detergent residue. The washing machine will use gentle mist of water which
helps dissolve detergents. 2.2.2 Capabilities
The washing machine can control the washing process such as the water intake, water temperature,
wash time, rinse performance and spin speed. This optimise the life span of the washing
... Get more on HelpWriting.net ...

Literature Review On Fuzzy Logic
CHAPTER 2
Literature Review
L.A. Zadeh, Fuzzy Sets [33] In 1965 the concept of fuzzy logic was first introduced by the Professor
Lotfi A. Zadeh in the University of California, Berkeley [33]. Fuzzy logic is a powerful design
system for implementing the artificial intelligence in the controller which provides simple and
intuitive method for software engineers to implement logic in complex systems. This concept had
been given in one amongst his research papers under the name Fuzzy logic or Fuzzy sets.
George J. Kilr and Bo Yuan [32] Fuzzy logic is a way to formalize the human decision capacity of
imprecise reasoning, or approximate reasoning. Such type of reasoning represents the human ability
to find out the reason approximately and judge ... Show more content on Helpwriting.net ...
Alhanjouri, M. and A. Alhaddad [25] The washing machine controller which was proposed by
Alhanjouri and Alhaddad‟s takes the type of dirt and degree of dirtiness as inputs and the wash time
is the only output of the system [25].
Ahmet Y¨or¨uko˘glu and Erdinc Altug [7] The fuzzy controller based washing machine is designed
using neural network which is based on fuzzy logic, neural network and its learning algorithm [7].
Wang Ai–zhen , Ren Guo–feng [8] They determine the wash time by observing the input variables
like Turbidity and turbidity change rate. In this paper the values are obtained from , the sensor of the
washing machine i.e. Turbidity and turbidity change rate which is then passed to the information
processing system , to process, the information was sent them to the controller. The value of input
parameters are translated into fuzzy variables by the process of fuzzification, using MCU,
accordance with the fuzzy inference rules and, the result is the fuzzy value. After defuzzification the
crisp value, the washing time is obtained which we modify by the concept of soft computing neural

Examples Of Kinematic Model Of Mobile Robot
Kinematic Model of Mobile Robot
The mobile robot has two drive wheels mounted on the same axis and assumed that each wheel is
perpendicular to the ground. The velocity of the center of mass of the robot is orthogonal to the
wheel axis.
Figure 1: Kinematic model of the mobile robot.
The Kinematic equations of the autonomous wheeled robot are
[█(x@y@θ_m )] = [■(cos θ_m&0@sinθ_m&0@0&1)] . [█(v@w)], (1)
[█(v@w)] =[■(r/2&r/2@r/D&(–r)/D)]. [█(V_R@V_L )], (2)
Combining (1) and (2), we get
[█(x@y@θ_m )]= [■(r/2 cos θ_m&r/2 cos θ_m@r/2 sinθ_m&r/2 sinθ_m@r/D&(–r)/D)] .
[█(V_R@V_L )] (3)
where x and y are the coordinates of the mass center of the robot, θ_m is the angle that represents
the current orientation of the robot, ... Show more content on Helpwriting.net ...
Electrical and Electronic Engineering Vol.11,No.1,2015.
[20] Leena.N,K.K.Saju,"A survey on path planning techniques for autonomous
mobilerobots",International Conference on Advances in Engineering & Technology – 2014(ICAET–
2014).
[21] Mohammed Algabri,Hassan Mathkour,Hedjar Ramdane,"Mobile Robot Navigation and
Obstacle–avoidance using ANFIS in Unknown Environment",International Journal of Computer
Applications (0975 – 8887),Volume 91 – No.14, April 2014.
[22] Dilip Kumar Pratihar, Kalyanmoy Deb, Amitabha Ghosh,"A genetic–fuzzy approach for mobile
robot navigation among moving obstacles",International Journal of Approximate Reasoning 20
(1999) 145–172.
[23] Angelo Martineza,Eddie Tunstela1,Mo Jamshidia,"Fuzzy logic based collision avoidance for a
mobile robot",Robotica,Volume 12,Issue 06,November 1994,pp 521–527.
[24] Nabeel K.Abid Al– Sahib,Ahmed Rahman Jasim,"Guiding Mobile Robot by Applying Fuzzy
Approach on Sonar Sensors",Al–Khwarizmi Engineering Journal,Vol. 6,No. 3,PP

Trust Based Misbehavior Detection in Wireless Sensor Networks
III. Problem Statement
This paper focuses on modeling and calculating trust between nodes in WSNs, based on sensed
continuous data to address security issues and deal with malicious and misbehavior nodes and for
assisting the decision–making process. A new trust model and a reputation system for WSNs can be
proposed. The trust model establishes the continuous version of the reputation system applied to
binary events and presents a new trust and reputation system for sensor Networks. This approach for
mixing of second hand information from neighboring nodes with directly observed information to
calculate trust between nodes in WSNs. Trust metrics can be used to evaluate the trust value of each
node in the clusters. Behaviors are monitored by monitoring node (MN). Monitoring node selected
at the next higher level of CH, this can also be changed dynamically along with CH. The main focus
of this paper is to develop a fuzzy theory based trust and reputation model for WSNs environment.
IV. System Model
A. Architecture
The architecture of our proposed system, consists of four major blocks namely,
i. Cluster Formation and CH selection ii. Information Gathering iii. Trust Evaluation and
Propagation iv. Misbehavior Detection
The detailed description about the architecture is as follows.
Fig. 2. Overall Architecture of the Proposed System
Fig.2. shows the overall architecture of the proposed work. In wireless sensor networks, the sensor
nodes are densely deployed in the

Iv.Proposed Model. This Section Introduced The Proposed
IV. PROPOSED MODEL This section introduced the proposed model for predicting failure of agile
software projects. The proposed model is composed of three parts: as shown below in figure 4.
– Review recent papers to extract important of failure factors in agile software projects.
– The proposed model uses multiple linear regressions to determine critical failure factors in agile
software projects.
– The proposed model uses fuzzy logic for predicting failure of agile software projects.
Figure4. The Proposed Model for Predicting Failure of Agile Software Projects The proposed model
is consisting of three parts as follow: determine of preliminary list of failure factors, determine final
list of failure factors and predict failure of ... Show more content on Helpwriting.net ...
– β0 is the y intercept Y represents the dependent variable (degree of effect in failure factors of agile
software projects) and x1, x2... xn are the independent variables (failure factors in agile software
projects).
Multiple linear regression introduced three main tables are regression statistics, ANOVA analysis
and coefficients analysis as shown below in figure 6.
Figure6. Three Main Tables in Multiple Linear Regressions
– Regression Statistics are consists of multiple correlation coefficient (R), determination coefficient,
adjusted R square, and finally standard error. Adjusted R square represents the percentage of
variance in the dependent variable (degree of effect in failure factors of agile software projects) can
be interpreted by the independent variables (failure factors in agile software projects).
– ANOVAAnalysis is consisting of regression, residual and significance F. whenever, significance F
is lower than 0.05, this means that the multiple linear regression helps to determine the most
significant attributes that impact failure factors in agile software projects.
– Coefficients Analysis is consisting of coefficients, standard error, t stat and p–value. If p–value in
failure factors < 0.05, then failure factors are accepted in agile software projects.
C. Fuzzy Logic Technique This section uses fuzzy logic technique to predict failure of

An Modern Methodology For Water Treatment Plant Affecting...
relationship is described by a collection of fuzzy control rules ( IF –THEN RULES) involving
linguistic variable [13].
6.1 METHODOLOGY
In this paper we introduce an modern methodology for water treatment plant affecting factors
analysis. The proposed expert system manages and explores the knowledge in this specific
application by reasoning on a database of facts by means of suitable inference rules. The proposed
comprehensive, homogenous framework uses a set of Fuzzy Inference Systems to interpret,
standardize and fuse heterogeneous data in order to estimate normalized factors. The FIS for
affecting factor analysis is divided into four main components: the fuzzification, knowledge base,
Inference engine, and the Defuzzification.
Fuzzy logic controller has four components:
1) Fuzzification: It transforms input into suitable linguistic value so that can be compared to the rule
in rule base.
2) Knowledge Base: It contains the knowledge in form of a set of rules to control the artificial
system. It is the collection of rules. The basic function of rule base is to provide the required
information to fuzzification module, the rule base and the defuzzification module. 'If ' part is called
antecedent and 'then ' part is called consequent.
3) Inference Engine: If control rules are relevant then it decides the input to the plant. The Inference
system provides the mechanism for invoking or referring to the rule base such that the proper rules
are fired on the situation.
4)

How The Quality Of Water Affected By The Parameter
necessary to compact decay scale formation, and fouling of heat transfer surfaces throughout the
reactor facility and support system [3].
The most common step in water treatment process includes;  COAGULATION/FLOCCULATION
 SEDIMENTATION  FILTRATION  DISINFECTION  SLUDGE DRYING 
FLUORIDATION  PH CORRECTION 5.0 WATER QUALITY PARAMETERS
In this paper we are going to analyze the quality of water affected by the parameter. The quality of
water is not equal and constant. Water quality is affected by different type of parameters such as
Colour, Turbidity, Total dissolved solids, Taste and odour, PH is the physical parameters. Chloride,
calcium, magnesium, sulphate, total hardness is the chemical parameters.
5.1 Colour
Colour is measured in Hazen units. Colour can originate from organic matter in the soil through or
over, which the water has passed. It is optimal parameter consisting in absorbing of a part of
spectrum of visible radiation by substances in dissolved in water, colloidal substances, and
suspended particles present in water.
5.2 Turbidity
Turbidity is dirtiness of water and is measured by a light scattering technique. Turbidity is a measure
of how particles suspended in water affect water clarity.
5.3 PH
PH is a measure of a solution 's acidity. In water, small numbers of water molecules (H2O) will
break apart or disassociate into hydrogen ions (H+) and hydroxide ions (OH–). Other compounds
entering the water may react with these, leaving an imbalance in the

Automatic Traffic Light Control
AUTOMATIC TRAFFIC LIGHT CONTROL SYSTEM
CONTENTS Introduction Traffic lights control system Design criteria and constraints Fuzzy logic
traffic lights controller system Input and output membership functions Fuzzy ... Show more content
on Helpwriting.net ...
The fuzzy logic controller is responsible for controlling the length of the green time according to the
traffic conditions. The state machine controls the sequence of states that the fuzzy traffic controller
should cycle through. There is one state for each phase of the traffic light. There is one default state
which takes place when no incoming traffic is detected. In the sequence of states, a state can be
skipped if there is no vehicle queues for the corresponding approach.
DESIGN CRITERIAAND CONSTRAINTS
In the development of the fuzzy traffic lights control system the following assumptions are made:
i) The junction is an isolated four–way junction with traffic coming from the north, west, south and
east directions. ii) When traffic from the north and south moves, traffic from the west and east stops
and vice versa. iii) No right and left turns are considered. iv) The fuzzy logic controller will observe
the density of the north and south traffic as one side and the west and east traffic as another side. v)
The East–West lane is assumed as the main approach.
Vi) The minimum and maximum time of green light is 2 seconds and 20 seconds respectively.
FUZZY LOGIC TRAFFIC LIGHTS CONTROLLER DESIGN
A fuzzy logic controller was designed for an isolated 4–lane traffic intersection: north, south, east
and west. In the traffic lights controller two fuzzy input variables are chosen: the

Importance Of Fuzzy Logic
6.1. IMPORTANCE OF FUZZY LOGIC Fuzzy logic is all about the relative importance of
precision: use as Fuzzy Logic Toolbox software with MATLAB technical computing software as a
tool for solving problems with fuzzy logic. Fuzzy logic is a fascinating area of research because it
does a good job of trading off between significance and precision something that humans have been
managing for a very long time. In this sense, fuzzy logic is both old and new because, although the
modern and methodical science of fuzzy logic is still young, the concept of fuzzy logic relies on
age–old skills of human reasoning. Fig 6.1 Fuzzy Description 6.2. USAGE OF FUZZY LOGIC
Fuzzy logic is a convenient way to map an input space to an output space. Mapping input to output
is the starting point for everything. Consider the following examples:
With information about how good your service was at a restaurant, a fuzzy logic system can tell you
what the tip should be.
With your specification of how hot you want the water, a fuzzy logic system can adjust the faucet
valve to the right setting.
With information about how far away the subject of your photograph is, a fuzzy logic system can
focus the lens for you.
49
... Show more content on Helpwriting.net ...
Between the input and the output, the preceding figure shows a black box that can contain any
number of things: fuzzy systems, linear systems, expert systems, neural networks, differential
equations, interpolated multi dimensional lookup tables, or even a spiritual advisor, just to name a
few of the possible options. Clearly the list could go on and on.Of the dozens of ways to make the
black box work, it turns out that fuzzy is often the very best way. As Lotfi Zadeh, who is considered
to be the father of fuzzy logic, once remarked: "In almost every case you can build the same product
without fuzzy logic, but fuzzy is faster and

Advantage Of Fuzzy Logic
Abstract: It is a well known widely accepted fact that the static spectrum allocation has led to the
under exploitation of the available frequency band. This problem can be mitigated by cognitive
radio technology. It is a concept which with its learning ability is aware about its environment and
intelligently adapts to it with objectives like reliable communication, efficient utilization of
spectrum and minimal or no interference among users.
Various techniques have been suggested in the research works to access the spectrum dynamically in
a cognitive radio environment. These techniques basically use the concepts of Artificial Intelligence.
Game theory, Markov chain models, Auctioning, Neural Networks, Multi Agent Systems and Fuzzy
Logic approaches ... Show more content on Helpwriting.net ...
Fuzzy logic is an attractive technique particularly in cases where target problems are difficult to
model with traditional mathematical models, but are easier to understand by human at the same time
With the characteristic of future cognitive radio in mind, the capability of fuzzy logic offer good
potential to be applied. Instead of using complicated mathematical formulations, fuzzy logic uses
human–understandable fuzzy sets and inference rules (e.g. IF, THEN, ELSE, AND, OR, NOT) to
obtain the solution that satisfies the desired system objectives. The main advantage of fuzzy logic is
its low complexity. Therefore, fuzzy logic is suitable for real–time cognitive radio applications in
which the response time is critical to system performance. A fuzzy logic control system can be used
to obtain the solution to a problem given imprecise, noisy, and incomplete input information. In
general, there are three major components in a fuzzy logic control system: fuzzifier, fuzzy logic
processor, and defuzzifier (Figure 4.4). While the fuzzifier is used to map the crisp inputs into fuzzy
sets, the fuzzy logic processor implements an inference engine to obtain the solution based on
predefined sets of rules. Then, the defuzzifier is applied to transform the solution to the crisp output.
Fuzzy logic is often used in decision making to select the best suited SU for spectrum access at a
given time. This technique when combined with neural networks is used in CR networks, multihop
routing or for detecting unauthorized

Advantages Of Fuzzy Logic System
From the above model we can make an overall idea about fuzzy logic system.Crisp input is dump
into the' Fuzzifier'..All those input should be defined as "set of input with different fuzzy ideas or
beliefs".All the input sets will be sent to Inference Engine which is connected with different fuzzy
rules which are nothing but some if–else statement.These if–else statement are used to define
different situations or conditions.So it is very obvious that we can impose different condition to the
fuzzy input sets.The main objective inference engine is matching the input data with different if else
statement and produce the fuzzy output.Next,all the sets of fuzzy output will be sent to the
Defuzzifier for the optimization of the given output sets ... Show more content on Helpwriting.net ...
This is a small scenario that is described which is based on sales amount. If the company wants to
impose any other condition,they can easily do that by using ths fuzzy xquery technique.so it is easy
to understand that by using fuzzy membership function we are getting more efficient output than the
output which was genetred by the simple query method.Fuzzy crisp input is taking the values on the
basis of given membership function and produce the optimized output by calculating them within
the aspiration level.This aspiration level is playing a very vital role because all the three fuzzy
funcaion are balanced by these aspiration

Electronic Logic Controller Essay
Speed Control of Separated Excited DC Motor using Fuzzy Logic Controller
Muaz Abdel Rahman Ismail1, Eltahir Mohamed Heessain2
Juba University, Department of Electronics,
College of Applied and Industrial Science. sudan
Muaz.abdoalrhman1@gmail.com.
2Department of Biomedical Engineering, Sudan university of Science and Technology,
ALsahva zalt road ,khartom ,sudan
Altahir2 33 ‫@ـــ‬yahoo.com.
Abstract: In this paper proposed the fuzzy logic controller as an alternative solution of conventional
PID controller for speed control of separated excited DC motor. Because many industrial
applications need constant speed of a DC motor operation, such as paper mills, steel rolling
mill...etc. But the various loads were effected on speed of DC motor, fuzzy logic controller gives
better results in governing the speed control of SEDC motor, the paper used MATLAB/Simulink to
simulation DC motor with fuzzy logic controller. The controller used seven memberships. Lastly, it's
found that the fuzzy logic controller provided better results for improving the dynamic behavior of
SEDC motor.
Keywords: DC Motor, PID Controller, Fuzzy logic controller, Chopper.
1. Introduction
DC motor is transducer device that converts the electrical energy to mechanical energy. Many
industrial applications were used such as robotic manipulators, electrical vehicles, steel rolling mills,
and electrical cranes, due to simpler, less precise, higher start torque characteristic and higher
response

Fuzzy Based Automated System For Predicting Viral Infections
Fuzzy based Automated System for Predicting Viral Infections(Chicken Pox, Swine Flu and
Dengue) Ravinkal Kaur
Dept. of computer science and engineering
CTITR
Jalandhar, India ravinkal93@gmail.com Sarabjit Kaur
Assistant Professor
Dept. of computer science and engineering
CTITR
Jalandhar, India er_sarabjitkaur35@rediffmail.com Virat Rehani
Assistant Professor
Dept. of computer applications
CTIMIT
Jalandhar, India vrehani@yahoo.com Abstract– Health protection is the improvement of health via
the diagnosis, treatment and prevention of disease, illness, injury, and other mental impairments in
human beings. This system is based on Fuzzy Logic, adopting Mamdani model as the fuzzy
inference mechanism and list of medical diseases. With diseases like swine flu and dengue fever,
chicken pox, on the rise, which have symptoms, are so closely associated that it sometimes become
practically Herculean task to differentiate between the above–scribed diseases based on symptoms.
Thus, it becomes inevitable to design such a system that would closely monitor the symptoms and
infer the disease based on fuzzy inference system. This work is done by assigning different
coefficients to each symptom of a disease and to predict and quantify the severity impact of the
recognized disease. For predicting, the cure time of a disease, based on the symptoms. Perdition of
cure time is clinically based on hypothetic studies and to estimate the cure time of a disease based
on the symptoms. This

Advantages Of Fuzzy Logic
In a narrow sense, fuzzy logic can be defined as a logical system, which is an expansion of multi–
valued logic. Whereas in a wider sense it is almost similar with fuzzy sets theory. It is a method for
computing based on "degrees of truth or fact" rather than the "true or false" (1 or 0). The idea of
fuzzy logic was first proposed by Dr. Lotfi Zadeh of the University of California at Berkeley in the
1965 [65].
4.1 FUZZY LOGIC SYSTEM
A fuzzy logic system (FLS) can be defined as the nonlinear mapping of an input data set to a scalar
output data [66]. It works like a way that human brain works. The data are get together and form a
number of partial facts or truths which are made aggregate further into higher level of truths. If these
truths crosses certain level of ... Show more content on Helpwriting.net ...
Fuzzy logic is a more sensitive approach without the complexity.
2) Fuzzy logic is flexible: In any given system, it is easy to coat on more functionality without being
start from scratch.
3) Fuzzy logic is tolerable about inexact data: In the nature or in any experimental process
everything is inexact. Fuzzy logic is made tolerable about all these things.
4) Fuzzy logic is able to model nonlinear functions of random complexity: A fuzzy controller is able
to match any kind of input and output values. This process is made mainly easy by adaptive
techniques which are available in Fuzzy Logic Toolbox of MATLAB software like Adaptive Neuro–
Fuzzy Inference Systems (ANFIS).
5) Fuzzy logic is based on general language: Fuzzy logic allows us to communicate with the system
using a common language of human like "If–then".
4.3 FUNCTION OF FUZZY LOGIC INFERENCE
The function of Fuzzy inference system is to interpret the values from the input vector and using
some set of rules, assigns these values to the output vector. This definition is clear from the figure

Image Fusion Technique Based on PCA and Fuzzy Logic Part 2...
mage fusion based on fuzzy sets
The fuzzy logic approach is widely used in image process–ing. The fuzzy logic gives decision rules
and fusion motivation for image fusion [17]. the two inputs images are converted into membership
values based on a set of predefined MFs, where the degree of membership of each input pixel to a
fuzzy set is determined. Then, the fusion operators are applied to the fuzzified images. The fusion
results are then converted back into pixel values using defuzzification.
1) Fuzzy sets: The fuzzy sets are used to describe the gray levels of the input images. we have two
inputs and one output. the two inputs are ; the first input is the Pan image and the second input is the
first principal component( PC
1
) of the MS ... Show more content on Helpwriting.net ...
The Mamdani fuzzy inference is widely used in applications, because of it has the simple structure
of defuzzification method Mamdani type min–imum sum mean of maximum which is
used.Defuzzification refers to the way a crisp value is extracted from a fuzzy set as a representative
value. The fuzzy rules in the form IF–THEN is used .The If–Then type fuzzy rules converts the
fuzzy input to the fuzzy output.
These rules are designed in the form of combination of inputs (Pan and pc
1
) represents as : (z) = max(x;y) =)fL;M!Mg (11) where x and y represenst pixel gray level values of
Pan and
PC
1 images respectively.The meaning of equation (11) that the pan gray level is low and the gray level
of pc
1
is meduim then the gray level of the fused image is meduim. we have 25 rules to fuse the pan image
and PC
1
we summerize as following :
TABLE I
FUZZY RULES OF IMAGE FUSION FUZZY LOGIC
VL L M H VH
L L M H VH
M M M H VH
H H H H VH
VH VH VH VH VH

The algorithm of image fusion by using fuzzy sets is implemented as the following:
Algorithm 2fuzzy logic image fusion algorithm
1: Input: M1 and M2
2: read first image in variable M1 ( Pan image) and calculate its size (rows : m1 and columns: n1)
3: read second image in variable M2 ( PC
1
); and calculate its size (rows : m2 and columns: n2)
4: M1and M2 Variables are images in matrix form where each pixel value is in the range from 0–
255.
5: Compare the size of both input images. If the two images are not of the

Comparative Analysis Of Different Maximum Power Point...
Comparative Analysis Of Different Maximum Power Point Techniques Using Boost Converter Joshi
Sarthak Krishna, SESGOIFE Diksal, Raigad1 Dr. B. R. Patil, Principal, Vishwaniketan IMEET,
Khalapur, Raigad2 Sharvari Sane, Head Of Department & Assistant Professor, SESGOIFE Diksal,
Raigad3 Abstract Current scenario of the globe is that sources for energy are lagging behind the
current demand so most concentration is on renewables. Solar performance efficiency mainly
depends on cell structure, maximum power point tracking technique and converter circuit used.
MPPT operation executes a key part in raising the strength of PV system. A p & o, incremental
conductance, Fuzzy based MPPT algorithmic principle is anticipated with a boost converter. Two in
and single out Mamdani's fuzzy framework with triangular membership is used to concoct the
controlled current. The anticipated procedure is upheld in MALAB/SIMULINK and in this way the
maximum power point tracking performance is evaluated. The anticipated system tracks the most in
operation reason with no wavering and enhanced exactness. The reproduction results demonstrate
the adequacy of the anticipated method. Keywords– MPPT method; p & o, incremental
conductance, fuzzy system; current control; DC–DC converter I. INTRODUCTION Solar energy is
a gateway that will ideally lead us far from our petroleum subordinate sources. The significant issue
with sun based board innovation is that the efficiencies for sun based power frameworks

My Rice Cooker: The Favorite Food Of My Food
Out of all the grains that are so vital to a healthy diet, I have always enjoyed rice the most. The sight
of soft and fluffy freshly cooked rice is something that always evokes my appetite. My family has
rice based meals 4 to 5 times each week and my kids enjoy those the most. That is why I can call it
the favorite food of my family.
I have used four rice cookers previously and although none of them had been unpleasant, I was
unable to find anything that really satisfied me. I wanted a rice maker that was not just affordable
but also very efficient. Moreover, what I wanted to avoid the most were the numerous buttons with
confusing functions that can turn a promising gadget into something of a nuisance.
When I wanted a new rice machine, I ... Show more content on Helpwriting.net ...
You can choose to cook rice, sushi or even risotto, at the touch of a single button. If this is not what
you call convenience, I don't know what is?
Amazing Capacity:
This rice machine by Zojirushi is an ideal choice for middle to small–sized families, or even single
people. Accommodating up to six cups of rice, I find it a real convenience. If guests are coming over
for dinner, you won't have to cook rice manually because this rice maker will take care of
everything.
Beautiful Colors:
Frankly speaking, it was the color that caught my attention the first time. Instead of the usual shades,
this ricecooker comes in excellent yellow, bringing some sunshine into your kitchen. Although it is
also available in white and metallic, I preferred yellow because of its uniqueness.
Non–Stick Interior:
Have you ever felt frustration of cleaning a rice cooker after use when the bottom layer of rice is
stuck to the cooking surface? If the answer is a yes, you are no exception. I had been experiencing
all the trouble myself, but now with a Zojirushi NS–XBC05YR, it is not a problem. It has a non–
stick interior that ensures you easy and effortless cleaning.
Insulated plastic

Particle Swarm Optimization And Its Range Of Operation
function, their numbers and intersection value determines accuracy of the tool and its range of
operation. If the membership function covers poles values from 0 to 4 then the maximum value for
pole is 4 and the least value is 0 and same thing happens for the constants. Each unit reduces an
order of two to order of one. If a transfer function of higher order is needed to be reduced the
operation is repeated several times. For example an order 8 to 2 function reduction will reduce the 8
poles into 4 poles then the 4 new poles into the final 2 poles. E. Particle Swarm Technique
Particle swarm optimization (PSO) is initialized with a group of random particles (solutions) and
then searches for optima by updating generations. In every iteration, each particle is updated by
following two "best" values. The first one is the best solution (fitness) each particle has achieved so
far, this value is called Pbest. Another "best" value that is tracked by the particle swarm optimizer is
the best value, obtained so far by any particle in the population. This best value is a global best and
called Gbest. Each particle consists of: Data representing a possible solution, a velocity value
indicating how much the Data can be changed, a personal best (Pbest) value indicating the closest
the particle 's Data has ever come to the Target.
The particles ' data could be anything. In the flocking birds' example above, the data would be the X,
Y, Z coordinates of each bird. The individual

Analysis Of Perturb & Observe, Incremental Conductance &...
Efficiency Comparison of Perturb & Observe, Incremental Conductance & Fuzzy Logic Controller
by Using Boost Converter and Interleaved Boost Converter abstract: Current scenario of the globe is
that sources for energy are lagging behind the current demand so most concentration is on
renewables. Solar performance efficiency mainly depends on cell structure, maximum power point
tracking technique and converter circuit used. MPPT operation executes a key part in raising the
strength of PV system. A p & o, incremental conductance, Fuzzy based MPPT algorithmic principle
is anticipated with a boost converter. Two in and single out Mamdani's fuzzy framework with
triangular membership is used to concoct the controlled current. The anticipated procedure is upheld
in MALAB/SIMULINK and in this way the maximum power point tracking performance is
evaluated. The anticipated system tracks the most in operation reason with no wavering and
enhanced exactness. The reproduction results demonstrate the adequacy of the anticipated method.
Keywords– MPPT method; p & o, incremental conductance, fuzzy system; current control; DC–DC
converter
Introduction
Solar energy is a gateway that will ideally lead us far from our petroleum subordinate sources. The
significant issue with sun based board innovation is that the efficiencies for sun based power
frameworks are still poor and the expenses per kilo–watt–hour (Kwh) are not focused, much of the
time, to rival conventional sources in the use. Solar

Hybrid Model Of Frfs And Rnn
4. THE PROPOSED HYBRID MODEL OF FRFS AND RNN Hybrid intelligent systems are vital
research areas for solving complex and multi–phase problems. Medical diagnostic field is
characterized by several sequential and related processes. Knowledge representation of diseases is
the essential goal of any medical system. The main sub–procedures are data selection, data
preprocessing, data transformation, pattern/rule induction and knowledge interpretation. Figure 4
introduces the main steps of knowledge representation system. Figure 4: Knowledge Extraction
Framework The proposed model is a fuzzy rough hybrid system for diagnosing breast cancer
patients. The diagnoses system is composed of preprocessing and classification phases. The hybrid
is consisted of three main sub modules. The first sub module is responsible for the selection process.
It preprocesses the data sets by eliminating the irrelative attributes. The framework utilizes a fuzzy
rough algorithm to handle the uncertainty nature of the medical data. The second sub module
produces an intelligent classifier of the diseases. It uses the rough neural network intelligence to
learn from the uncertain reduced data set. After training, the rough neural network becomes the
intelligent classifier of the unseen cases to predict their medical condition of the illness. The third
sub module measures the accuracy and time complexities of the intelligent classifier by the test data
set. Figure 5 shows the main sub modules and their

The Field Of Serpentine Belt Drive System
The first solution is Parameter Estimation. Actually, in certain engineering problems, vibration
control for an axially moving string focuses on the vibration isolation problem. Controllers are
designed to restrict vibration resulting from external disturbances, such as support pulley
eccentricity or aerodynamic excitation, to areas not requiring high precision positioning.
Some basic works have been done in the field of serpentine belt drives are researches on the
vibration characteristics of axially moving string. Beikmann et al., (1996) applied a mathematical
model to examine the transverse vibration and stability of coupled belt–tensioner systems.
Meanwhile, they modeled and analyzed the serpentine belt drive systems with a dynamic tensioner
shown as figure 3.1.
Fig. 1 three–pulley serpentine belt drive system
This model system includes the essential components mounted in serpentine belt drive systems,
which is a driving pulley, a driven pulley, a dynamic tensioner. Assumptions are made to simplify
the modeling of the belt drive system: (i) Damping is negligible, (ii) Belt bending stiﬀness is
negligible and hence belts are modeled as strings, (iii) Axial translation speed of the belt, c, is
constant and uniform, (iv) Belt slippage is negligible, (v) Pulleys other than the tensioner have fixed
axes, (vi) Belt/pulley contact points are those calculated at equilibrium.
Hamilaton's principle can be applied to derive governing equations and boundary conditions. The

Classification Between The Objects Is Easy Task For Humans
Classification between the objects is easy task for humans but it has proved to be a complex
problem for machines. The raise of high–capacity computers, the availability of high quality and
low–priced video cameras, and the increasing need for automatic video analysis has generated an
interest in object classification algorithms. A simple classification system consists of a camera fixed
high above the interested zone, where images are captured and consequently processed.
Classification includes image sensors, image preprocessing, object detection, object segmentation,
feature extraction and object classification. Classification system consists of database that contains
predefined patterns that compares with detected object to classify in to proper category. Image
classification is an important and challenging task in various application domains, including
biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual inspection,
robot navigation, and remote sensing. Fig. 1.1 Steps for image classification Classification process
consists of following steps a) Pre–processing– atmospheric correction, noise removal, image
transformation, main component analysis etc. b) Detection and extraction of a object– Detection
includes detection of position and other characteristics of moving object image obtained from
camera. And in extraction, from the detected object estimating the trajectory of the object in the
image plane. c) Training: Selection of the

Examples Of Contradictions In Fuzzy Logic
Contradictions in Fuzzy Logic
Kamp ([qtd. in Sauerland]) argue that it is absurd that p ∧ p has the same truth value as p ∧ ¬p if p is
0.5, and the latter statement cannot have a truth value of 0.5, becauses "how could a logical
contradiction be true to any degree?" (qtd. in Sauerland 187). Sauerland ([year]) however thinks that
this is not the case: if p contains a vague variable, the statement does not need to be a contradiction
to be intuitive, e.g. What I did was smart and not smart, or Bea is both tall and not tall are statements
which are contradictions, and they still make sense although they are vague (186). I agree that What
I did was smart and not smart makes sense in vagueness and fuzzy logic: this statement might be
false, or true, ... Show more content on Helpwriting.net ...
The same holds for sentences like What I did was smart and not smart (cf. Sauerland ([citation?]),
but sentences like he is tall and not tall are different: in this case you cannot say that the person is
tall in some respects and not tall in other respects (except maybe when you argue that the person
turns out to be a child who is tall compared to other children but not tall compared to adults), but
more commonly you express an average height by this, which is between your personal crisp
boundaries of tall and not tall respectively. Still, both sentences He is nice and not nice and He is tall
and not tall are of the form p ∧ ¬p, and are not considered contradictions with a truth value of 0. But
if you consider He is nice and not nice as the person being nice and not nice in the same respect,
then of course the contradiction would be obviously present, in which case the truth value would
indeed be 0 (I do not think that you could interpret He is tall and not tall in a way such that its truth
degree would be 0): it would then be reasonable to interpret He is nice (in some respects) and not
nice (in other respects) as rather p ∧ ¬q than p ∧ ¬p, because the different contexts in which he is
nice and not nice might be seen as concepts which are not actually related, that is, the situations in

Optimization For The Design Of Load Frequency Control
PSO OPTIMIZED FLC FOR THE DESIGN OF LOAD FREQUENCY CONTROL
M. MAHAMMED JABEER
Associate Professor, Department of Electrical & Electronics Engineering,
AVR & SVR Engineering College, Kurnool, Andhra Pradesh.
Abstract: Load frequency control problem is considered as one of the most important issues in the
design & operation of power systems. Due to lack of good efficiency in parameters variation
conditions, working conditions of system and non–linear factors, a simple PI controller is not
suitable in industrial applications. Instead, fuzzy controllers can be used in order to enhance the
performances of the systems. In this paper, the use of the optimized type–1 fuzzy logic controller
using Particle Swarm Optimization (PSO) algorithm is proposed to solve the load frequency control
problem. To the best of our knowledge, the PSO optimization of fuzzy type–1 controller in order to
solve load–frequency control problem, has not been investigated so far. The proposed controller has
good performance and is capable to solve the load–frequency control problem in conditions of wide
variations of system parameters and nonlinear factors such as generation rate constraint. Simulation
results show that the optimized fuzzy controller proposed in this paper exhibits better performance
compared to PI controller in damping of system deviations.
Key words: power system, load frequency control, type–1 fuzzy logic controller, PSO algorithm.
1. Introduction
The main aim of power systems is

The Pros And Cons Of Fuzzy Logic
As mentioned in the previous section of this chapter trend analysis, analyze just changes in the past
years in electricity demand and utilize it to predict future electricity demand, but there is no process
to explain why these changes happened. End users and behavior of end user are not important in this
model. But in end use method of forecasting, statistical information related to customers along with
amount of change act as the basis for the forecast.
While in Economical methods, the results are estimated upon the relationship between dependent
variables and factors that influence electricity consumption. Time series and least–square method
are used to estimate the relationship. Comparison of these three parametric model shows that ...
Show more content on Helpwriting.net ...
2.3 Factors Affecting Accurate Demand Forecast
The operation of electricity system is strongly influenced by the accuracy of demand forecast as
economy and control of electric power system is quite sensitive to forecasting errors [44–45]. The
four important factors affecting load forecast are:
I. Weather conditions Electricity demand has a strong correlation to weather. To develop an efficient
and accurate demand forecasting model for electricity much effort has been put to find a relationship
between the weather and the demand of electricity. The change in comforts of customer due to
change in weather conditions resulting in usage of appliances likes air conditioner, space heater and
water heater. It also includes use of agricultural appliances for irrigation. The pattern of demand
differs greatly in the areas with large meteorological difference during summer and winter. Dry and
wet temperature, humidity, dew point, wind speed, wind direction, sunshine and amount of
precipitation are common weather parameters that influence electricity demand. Among the weather
variables listed above, two composite weather variable functions, the cooling degree days and
heating degree days are broadly used by utility

The Fuzzy Logic System
Artificial intelligence and machine learning techniques provide a qualitative as well as quantitative
assessment of the power system.
1. Fuzzy Logic: The Fuzzy logic system (FLS) is a logic system which represents reasons and
knowledge in a fuzzy manner for reasoning under uncertainty or describes in imprecise manner for
human interpretation. Not like Boolean logic and classic logic which assumes that entire fact is
either true or false, but fuzzy logic allows Boolean logic to tackle with vague and imprecise
expressions of human understanding. Not like the classic logic systems, it models the reasoning for
imprecision model that plays important role in ability of human knowledge to understand an
estimated or inexact answer for a question which is based on store of knowledge which is
approximate, not complete or totally unreliable. It is the best approach and way to go for fuzzy logic
when it is too difficult to ... Show more content on Helpwriting.net ...
Knowledge of human experts forms the base of the accuracy of fuzzy logic systems (FIS). The
results of post contingent state of line power flows and performance indices are obtained using
Newton Raphson or any other load flow method .The membership functions for these post
contingent quantities are first recognized and defined and with these formed membership functions,
the computation of overall severity index is done to obtain the contingency ranking. For each post
contingent quantities which is obtained by the conventional load flow method is known by different
linguistic variable and with the membership function associated with it. The inputs to the fuzzy
inference system are line loadings, and voltage profiles indices and the outputs to the same FIS are
the severity indices, which are computed using the simple set of rules of Fuzzy. The post contingent
quantities of line flows and bus voltage must be

Fuzzy Logic Is a Way to Deal with Imprecise Concepts Essay
As I sit down to write this paper, I am also waiting for my laundry to finish. I set the machine to
"auto" load size and dumped my clothes in, oblivious to the actual size of the load. The machine can
calculate the size of the load for me, and assure that my clothes are washed at the proper
temperature for the proper amount of time. This is accomplished through the use of what is referred
to as soft computing, pioneered by a man named Lofti Zadeh (Peterson). Lofti Zadeh was born in
Baku, Azerbaijan in 1921. The son of an Iranian journalist and a Russian physician, Zadeh's early
life was spent under the influence of Soviet ideas. In an interview with Betty Blair, Zadeh speaks of
how the Soviet schools of his childhood placed great ... Show more content on Helpwriting.net ...
The choice is no longer just zero or one." Fuzzy logic allows for "fuzzy sets," which do not rigidly
follow a "yes or no" logic when attempting to determine the elements of such a set. Fuzzy sets can
include partial elements; elements that may or may not belong to the set depending on other
circumstances (Peterson). Because of this flexibility, fuzzy sets are used in soft computing, a process
engineers now use in many modern home appliances. Prior to the 1970's, fuzzy logic had not been
put into practical, real–world use. This changed when many Japanese manufacturers began to
implement simple fuzzy systems in their household products, leading to the vast amount of items
which make use of soft computing today (Zadeh).
To go back to a previous example, my washing machine determines which type of cycle to use
based on the weight of the clothing. A "small" load or a "medium" load is not rigidly defined, but the
fuzzy logic allows the washing machine to approximate the type of cycle it should use. An amount
of clothes on the cusp of both "small" and "medium" could be placed in either category, depending
on other variables such as fabric mix and the amount of detergent ("Fuzzy logic"). In this specific
example fuzzy logic and soft computing can lead to errors, such as a wash cycle erroneously
stopping after the clothes become heavier due to

Quantum Population Based Meta Heuristic
In physics gravitation is the tendency of agent with object to accelerate towards each other. In the
Newtonian law each object attracts every other object by a gravitational force. Gravitational search
algorithm is one of the newest stochastic population based meta heuristic that has been inspired by
newtanion laws of gravity and motion. The algorithm is consisted of collection of searcher agents
that interact with each other through the gravity force. The agents are deliberated as objects and their
execution is measured by their masses. The gravity force induces a global motion where all objects
proceed towards other objects with heavier masses. The slow motion of heavier masses guarantees
the victimization step of the algorithm and corresponds to good solutions. The masses are actually
obeying the law of gravity as shown in Equation (3.1) and the law of motion in Equation (3.2).
F = G (M1M2 / R2) (3.1) a = F/M (2) (3.2)
Based on Equation (3.1), F represents the magnitude of the gravitational force, G is gravitational
constant, M1 and M2 are the mass of the first and second objects and R is the distance between the
two objects. Equation (3.1) shows that in the Newton law of gravity, the gravitational force between
two objects is

What Is The Performance Of The Proposed Control Algorithm...
This section investigates the performance of the proposed control algorithm of DSTATCOM by
means of computer simulations in MATLAB/Simulink environment. Tracking and harmonic
decomposition capability of the proposed AANF are evaluated in this section, and the performance
of the whole system for load balancing, the harmonic compensation, the neutral current
compensation, and the power factor correction will be investigated in Section 5.
4.1 Initiatory performance
Consider the input signal of the proposed AANF as: y(t)=sin⁡
(ω_0 t+φ_1 )+0.2 sin⁡
(5ω_0 t+φ_5 )+0.3
sin⁡
(7ω_0 t+φ_7 )+0.3 sin⁡
(30ω_0 t+φ_30) (21) where〖 ω〗_0=100 π rad/s and the initial phase
angles φ_i's are selected randomly between zero and 2π rad. The response of the system ... Show
more content on Helpwriting.net ...
8.
DSTATCOM Simulation Results
The performance of the four–leg DSTATCOM using AANF–based control algorithm is
demonstrated for power factor correction and voltage regulation along with harmonic reduction,
load balancing, and neutral current compensation. The model is analyzed for linear and non–linear
loads under non–ideal supply conditions.
The three–phase unbalanced distorted source voltages consisting of the negative–sequence
component and harmonic voltage components are expressed in (23).
{█(V_a=〖250 sin〗⁡
(ωt)+25 sin⁡
(ωt)+3.7 sin⁡
(3ωt)+18.6 sin⁡
(5ωt–〖120〗^° ) @+4.5 sin⁡
〖(7ωt)+3.1 sin⁡
(11ωt–〖120〗^° ) 〗 @V_b=〖250 sin〗⁡
(ωt–〖120〗^° )+25 sin⁡
(ωt+〖120〗^°
)+3.7 sin⁡
(3ωt)+18.6 sin⁡
(5ωt) @+4.5 sin⁡
〖(7ωt–〖120〗^° )+3.1 sin⁡
〖(11ωt)〗〗 @V_c=〖250
sin〗⁡
(ωt+〖120〗^° )+25 sin⁡
(ωt–〖120〗^° )+3.7 sin⁡
(3ωt)+18.6 sin⁡
(5ωt+〖120〗^° )@+4.5 sin⁡
〖(7ωt+〖120〗^° )+3.1 sin⁡
〖(11ωt–〖120〗^°)〗〗 )┤ (23)
5.1 PFC operation of DSTATCOM under linear lagging power factor load condition (Case A)
The power factor correction and load balancing simulation results for the proposed AANF–based
control algorithm of the four–leg DSTATCOM under unbalanced distorted supply voltages is
discussed here. At t= 0.4 s, phase 'A' and at t= 0.5s phase 'B' are disconnected, respectively, and at t=
0.7 s and t= 0.8 s phases 'A' and 'B' are applied again. Fig.

The Fuzzy Inference Rules ( Rule Block )
Now we define the fuzzy inference rules (Rule Block –1 –RB1) to the first four parameters
mentioned in the table no –1 as inputs with the physical quality of the water as output in the
following way using Mamdani method Table –2 Output result from the application of IF –THEN
rules,with membership function. Now we define the fuzzy inference rules (Rule Block –2 –RB2) to
the next five parameters mentioned in the table no –1 as inputs with the chemical quality of the
water as output in the following way using Mamdani method Table –3 Analyses of water quality
output values Now we define the fuzzy inference rules (Rule Block –3 –RB3) to the parameters,
physical and chemical as inputs with the complete quality of the water as output in the following
way using Mamdani method Table –4 Analyses of water quality output values STEP: 4
Defuzzification : Defuzzification is processes to get a non fuzzy control action that best represent
the possibility distribution of an inferred fuzzy control action [12]. CENTRE OF AREA METHOD
The widely used COA strategy generates the centre of gravity of the possibility distribution of a
fuzzy set C .The method gives . Figure–11 Graph of water quality output values In this above figure
the physical parameter as input values.It shows the out put result. Figure–12 Graph of water quality
output values In this above figure the chemical parameter as input values.It shows the out put result
Figure–13 Graph of water quality output values 7.0 RESULT AND

The Induction Machine ( Im ) Essay
Electrical machines have gained a distinctive interest by experts because their advantages are
indisputable because of their ability to adapt to any environment and their efficient efficiency, thus
exceeding other non–electric actuators. The induction machine (IM) is currently the most widely
used electrical machine in both domestic and industrial applications. Its main advantage lies in its
simplicity of mechanical and electrical design (absence of rotor winding (cage machine) and
collector, simple structure, robust and easy to build .....). However, these advantages are
accompanied by a high degree of physical complexity, linked to the electromagnetic coupling
between the magnitudes of the stator and those of the rotor. This is why, for a long time, IM was
only used in constant speed drives.(El–kharashi and El–dessouki, 2014).
It is only after the revolution in electronics–computing and power electronics that the field of
variable speed drive by AC machines has grown tremendously. Especially since digital processors,
such as Digital Signal Processors (DSPs), Field Programmable Gate Arrays (FPGAs), which
specialize in driving electrical machines, have facilitated experimental implantation. It is not by
chance that the work on the IM is the subject of intense research in several fields, be it for the
synthesis of control laws, for the calculation and optimization of yield or for the development of a
strategy of diagnosis and detection of failures. This is confirmed since

Disadvantages Of Fuzzy Logic
Chapter 2
2.1 Fuzzy Logic Controller Fuzzy logic is a type of multi valued logic. It deals with approximate
reasoning rather than precise. Fuzzy logic derived from fuzzy set theory. Fuzzy logic was first
proposed by Lotfi Zadeh in 1965. Fuzzy logic has currently used in control theory, artificial
intelligence systems specially to control complex aircraft engines and control surfaces, helicopter
control, messile guidance, automatic transmission, wheel slip control, auto focus cameras and
washing machines, railway engines for smoother drive and fuel consumption and many industrial
processes. Fuzzy logic provide better results if we compared it with PID controller. Fuzzy set of
theory represent the human reasoning with knowledge that is almost impossible to represent in
quantitative measures or for that control plants that are hard to control or ill ... Show more content
on Helpwriting.net ...
Haman knowledge and experience can be implemented using linguistic rules.
Non linear plants can be controlled.
It can also control fast processes.
Disadvantages of fuzzy control–
Human knowledge is often incomplete and episodic as compared to systematic way.
If the model is not known then it is impossible to achieve the stability of the controller system.
Sometime rules are mismatched and non coherent.
In complex operation fuzzification and defuzzification take long time.
Fuzzy expert systems–There are two famous type of system currently used in fuzzy logic
Mamdani fuzzy inference
Sugeno fuzzy inference
2.2 Mamdani fuzzy inference.
The most common method is used currently is fuzzy inference system. In 1975, Professor Ebrahim
Mamdani of London University introduced first time fuzzy systems to control a steam engine and
boiler combination. He applied a set of fuzzy rules experienced human operators. The mamdani
system usually done in four

Fuzzy Logic: The Principles Of Fuzzy Logic Control System
4. FUZZY LOGIC CONTROLLER
4.1 Introduction
Fuzzy Logic provides a completely different approach. One can concentrate on solving the problem
rather than trying to model the system mathematically, if that is even possible. This almost
invariably leads to quicker, cheaper solutions. Once understood, this technology is not difficult to
implement and the results are usually quite surprising and more than satisfactory.
Fuzzy logic is a complex mathematical method that allows solving difficult simulated problems with
many inputs and output variables. Fuzzy logic is able to give results in the form of recommendation
for a specific interval of output state, so it is essential that this mathematical method is strictly
distinguished from the more familiar logics, such as Boolean algebra. This paper contains a basic
overview of the principles of fuzzy logic.
4.2 Fuzzy Logic Control System
Fuzzy logic allows to lower complexity by allowing the use of imperfect information in sensible
way. It can be implemented in hardware, software, or a combination of both. In other words, fuzzy
logic approach to problems' control mimics how a person would make decisions, only much faster.
... Show more content on Helpwriting.net ...
For example, speed can be represented by value 5 m/s or by description "slow". Term "slow" can
have different meaning if used by different persons and must be interpreted with respect to the
observed environment. Some values are easy to classify, while others can be difficult to determine
because of human understanding of different situations. One can say "slow", while other can say
"not fast" when describing the same speed. These differences can be distinguished with help of so–
called fuzzy sets. Usually fuzzy logic control system is created from four major elements

Essay about Mind And Machine
Mind and Machine: The Essay Technology has traditionally evolved as the result of human needs.
Invention, when prized and rewarded, will invariably rise–up to meet the free market demands of
society. It is in this realm that Artificial Intelligence research and the resultant expert systems have
been forged. Much of the material that relates to the field of Artificial Intelligence deals with human
psychology and the nature of consciousness. Exhaustive debate on consciousness and the
possibilities of consciousnessness in machines has adequately, in my opinion, revealed that it is most
unlikely that we will ever converse or interract with a machine of artificial consciousness. In John
Searle's collection of lectures, Minds, Brains and ... Show more content on Helpwriting.net ...
Proposition four is where the ends will meet the means. It purports that when we are able to finally
understand the brain, we will be able to duplicate its functions. Thus, if we replicate the
computational power of the mind, we will then understand it. Through argument and
experimentation, Searle is able to refute or severely diminish these propositions. Searle argues that
machines may well be able to "understand" syntax, but not the semantics, or meaning
communicated thereby. Esentially, he makes his point by citing the famous "Chinese Room
Thought Experiment." It is here he demonstrates that a "computer" (a non–chinese
speaker, a book of rules and the chinese symbols) can fool a native speaker, but have no idea what
he is saying. By proving that entities don't have to understand what they are processing to appear as
understanding refutes proposition one. Proposition two is refuted by the simple fact that there are no
artificial minds or mind–like devices. Proposition two is thus a matter of science fiction rather than a
plausible theory A good chess program, like my (as yet undefeated) Chessmaster 4000 Trubo refutes
proposition three by passing a Turing test. It appears to be intelligent, but I know it beats me through
number crunching and symbol manipulation. The Chessmaster 4000 example is also an adequate
refutation of Professor Simon's fourth proposition: "you can understand a process if you can
reproduce

Essay On Japanese Food
Although I m not Japanese, I have lived in Japan for several years. I have to admit, I fell in love with
Japanese cuisine. The taste and the aroma fascinates me and I can not help going back to Japanese
restaurants every now and then. More than anything, I love rice!
When I used to came back home from work, all I wanted to eat was a meals that had rice in it. One
may call me obsessed but what do you expect from a man who ate nothing but rice for many years –
and loved it! The problem I faced was that, no matter how much I tried, I never got that restaurant
like flavor with my rice. I switched rice cookers, even tried alternative recipes, but nothing worked
for me.
One day I was just skimming the shelves at the mall when something caught my eye. It was the
Zojirushi NS–TGC10 rice warmer and cooker. A lot of attractive features were listed and when I
checked the price, I was shocked because it was almost half of what I had paid for all the different
rice makers so far. I decided to give it a try. ... Show more content on Helpwriting.net ...
It was all I could ask for. With just the aroma and taste I have been looking for, I call it an ideal
choice for everyone looking for a nice rice maker.
The purpose of writing this review is to tell people about my experience with this efficient device
and help them purchase something that they can cherish for years to come.
Zojirushi Rice Cooker– Outstanding Features:
It has many attractive aspects to it but the ones I love the most are as follows:
5.5 cup rice cooker with advanced Fuzzy Logic Technology
Variety of cooking functions
LCD control panel
Automatic 'keep warm' mode
Stainless steel

Fuzzy Systems Essay
Preface
This article is written for an intended audience of undergraduate or graduate students, this article
provides an introduction to, and an overview of what fuzzy systems are. Presented in this article is
an acknowledgment of the contributions that fuzzy systems are making to the Artificial Intelligence
discipline, as well as examples of fuzzy systems which are in use today.
Abstract
The concept and implementation of fuzzy systems is part of the natural course of evolution for
humans who are a part of a society where access to information is plentiful but efficient utilization
of massive amounts of information is power. To get at the information, we need systems which can
understand what we need, rather than for us to understand ... Show more content on Helpwriting.net
...
The origin of fuzzy systems can be traced to the first introduction of formal logic by Aristotle. Logic
introduced by Aristotle was considered to be bilevel logic of either true or false, and is
acknowledged to be the foundation of most of the growth of the sciences which are known today.[1]
However, it was Plato who is accredited with laying the actual foundations for fuzzy logic when he
proposed that there exists a third area beyond true and false where things were not always true or
always false.[3]
Digital computers in use today are based upon the ideas of Aristotle, where a single bit of
information found in a computer is considered either true or false, that the transistor gate is either
turned on or off. Many fundamental aspects of society today are based upon concepts of right or
wrong, innocent or guilty, a member of a group or not a member of some group.
The human race has toyed with and enjoyed the concept of a perfect society, where everything was
easily classified into good or bad, right or wrong, true or false and has created computers to respond
to that type of idealistic and simplistic world. This philosophy has served the human race well as
computers were created to operate in very predictable ways that were

Haack On Fuzzy Logic Essay
Haack On Fuzzy Logic
ABSTRACT: Much of the progress in modern logic beyond Aristotle is due to the invention of a
precise and powerful formalism, and this is why Haack is reluctant to weaken it. What motivates her
to regard deviant and fuzzy logic as extensions rather than rivals of classical logic is its
demonstrated capacity for refinement and progress. Thus she sharply distinguishes between a logic
dealing with fuzzy concepts (she accepts), and one which is itself fuzzy, i.e., where "true" and
"false" cease to be precise concepts (she rejects). While it is often more convenient to retain as much
as possible of classical logic because of its simplicity and familiarity, there is nothing in the
hermeneutical view of logic to render it ... Show more content on Helpwriting.net ...
If however fuzzy logic is taken to be, as Zadeh and his followers claim, a rival theory, fuzzy
technology is irrelevant to its philosophical bona fides. (2) The idea is to accommodate vagueness
within the framework of classical logic by means of a non–classical semantics in which vague
evidence counts as true just in case it would be true for all the ways of making it precise.
After explaining how radically fuzzy logic departs not only from classical logic, but also from the
classical conception of what logic is and does, Haack criticizes fuzzy logic for its methodological
extravagances and its linguistic inconsistencies. She argues that despite the considerable new
complexities it introduces, fuzzy logic does not avoid but actually requires the imposition of
artificial precision – the very fault for which Zadeh blames classical logic. She also points out that
the linguistic evidence does not support the main contention motivating fuzzy logic that "true" and
"false" like "bald" and "tall" are predicates of degree. Linguistic, metaphysical, and methodological
considerations all speak against degrees of truth. (3) Haack concludes that fuzzy logic is not a viable
competitor of classical logic. A process of weighted averaging "defuzzifies" – Carnap and Haack
might say, "precisifies" –

Brief Explanation of the Basic Framework of the Principal...
PRELIMINARIES
This section expands a brief explanation of the basic frame–work of the principal component
analysis and fuzzy logic, along with some of the key basic concepts.
A. The principal component analysis (PCA)
The Principal component analysis (PCA) is an essential technique in data compression and feature
reduction [13] and it is a statistical technique applied to reduce a set of correlated variables to
smaller uncorrelated variables to each other. PCA is considered as special transformation which
produces the principal components (PCs) Known as eigenvectors. PCs are sorted decreasing i.e. the
first prin–cipal component (PC
1
) has largest of the variance. That is meanvar(PC1 );var(PC2
);var(PC3
);var(PCp
), where var(PCi ) ... Show more content on Helpwriting.net ...
PCA is considered as special transformation which pro–duces the principal components (PCs)
Known as eigenvectors
.PCs are sorted decreasing i.e. the first principal component
(PC
1) has largest of the variance; That is mean var (PC
1
) var (PC
2) var (PC
3) ... var (PC
p), where var(PC i ) expresses the variance of(PC i ) [14]. The PCA has characteristics and ability to
reduce redundancy and uncertainty. So this paper used the PCA as the preprocessing step on the
multispectral images to reduce the redundancy information and focus on the component; that have a
significant impact on the data.
B. Fuzzy logic
Zadeh [15] introduced the concept of fuzzy logic to present vagueness in linguistics, and to
implement and express human knowledge and inference capability in a natural way. The fuzzy logic
starts with the concept of fuzzy sets which is defined as a set without a crisp. It is clearly defined
boundary and can contain elements with only a partial degree of member–ship.The main power of
fuzzy logic image processing is in the middle step (membership function) [16] A membership
function defines how each value in the input space is mapped to a membership value (or degree of
membership) between the range of 0 and 1. LetXbe the input space andxbe a generic element ofX. A

classical set Ais defined as a collection of elements or objectsx2X, such that each xcan either belong
or not belong to the setA, AvX.

Fuzzy Logic Technique For Pq Improvement
Fuzzy Logic Technique for PQ Improvement by UPQC for Renewable Energy Recourse's
1BJ SHIVA RAMA KRISHNA RAO, PG STUDENT, BRILLIANT GROUP OF INSTITUTIONS,
HYD
2O SWATHI, PG STUDENT, BRILLIANT GROUP OF INSTITUTIONS, HYD
3B. NAGI REDDY, ASST.PROF, BRILLIANT GROUP OF INSTITUTIONS, HYD
ABSTRACT: –– In this a fuzzy logic control technique has been proposed for power quality
improvement by using UPQC The UPQC is controlled to regulate the WF terminal voltage, and to
mitigate voltage fluctuations at the point of common coupling (PCC), caused by system load
changes and pulsating WF generated power, respectively. In order to reduce the voltage fluctuations
that may cause flicker, and improve WF terminal voltage regulation, several solutions have been
posed. The voltage regulation at WF terminal is conducted using the UPQC series converter, by
voltage injection in phase with PCC voltage. On the other hand, the shunt converter is used to
filter the WF generated power to prevent voltage fluctuations, requiring active and reactive power
handling capability. The sharing of active power between converters is managed through the
common DC link. A customized internal fuzzy logic control scheme of the UPQC device was
developed to regulate the voltage in the WF terminals, and to mitigate voltage fluctuations at grid
side. Simulation results show the effectiveness of the proposed compensation strategy for the
enhancement of Power Quality.
Keywords – Wind Energy, UPQC, voltage

Essay On Fuzzy Logic

Recommended

Recommended

More Related Content

Similar to Essay On Fuzzy Logic

Similar to Essay On Fuzzy Logic (15)

More from Lucy Nader

More from Lucy Nader (20)

Recently uploaded

Recently uploaded (20)

Essay On Fuzzy Logic