# CHAPTER 5 FUZZY LOGIC CONTROLLER FOR THE CONICAL TANK PROCESS

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 99 CHAPTER 5 FUZZY LOGIC CONTROLLER FOR THE CONICAL TANK PROCESS 5.1 INTRODUCTION Fuzzy Logic Controllers are normally preferred for control applications, due to their fault tolerance, knowledge representation, expert knowledge, nonlinearity, uncertainty, real time operation, etc. The Fuzzy Logic Controller basically consists of three elements, viz, fuzzification, fuzzy inference engine, and defuzzification. To control the nonlinear conical tank process, the Mamdani type of Fuzzy Logic Controller is designed. In this work, the Fuzzy Logic Controller is selected as a controller for the nonlinear conical tank process, and its performance is compared with that of the Neuro tuned PI Controller. The simulation results are obtained by the servo, regulatory and servo-regulatory operations, for the above mentioned controllers, for the conical tank level process. 5.2 BLOCK DIAGRAM REPRESENTATION OF THE FUZZY LOGIC CONTROLLER The fuzzy set theory concept was proposed by Lotfi A.Zadeh of the University of California in his paper published in The fuzzy logic representations found in the Fuzzy set theory, try to capture the way humans represent and reason with real world knowledge in the context of uncertainty. The word fuzziness means, vagueness, cloudiness, unclearness and

2 100 indistinctness ; it was used in Washing machines, Refrigerators, Nuclear reactors, Automobiles, and Traffic Light Control, and many such uses were developed by the Japanese. The input variables in the fuzzy control system can be mapped into the set of membership functions which is termed as a Fuzzy set. The set of variables obeys the Boolean logic expression, and has the same characteristics of individual elements called Crisp set. The block diagram representation of a Fuzzy Logic Controller is shown in Figure 5.1. The Fuzzy Logic Controller consists of three basic elements: Fuzzification, Fuzzy Inference Engine, Defuzzification. Knowledge Base Rule Base Reference Input + _ Fuzzification Fuzzy Inference Engine Defuzzification Process Controlled Output Sensor Figure 5.1 Block diagram representation of Fuzzy Logic Controller 1. Fuzzification The Fuzzy Logic Controller requires that each input or output variable, which defines the control surface, can be expressed in fuzzy set notations using Linguistic levels. The linguistic values of each input and output variable divide its universe of discourse into adjacent intervals, to form the membership functions. The membership value denotes the extent to which

3 101 a variable belongs to a particular level. Fuzzification is the process of converting the crisp value into a fuzzy one. The different methods used for fuzzification may be : Ranking order method, Intuition, Inference, Neural networks, Genetic Algorithm, Inductive reasoning method, etc. For this work, the rank ordering method is preferred and used. 2. Knowledge based system The Knowledge based system consists of a data base and a rule base. Data based knowledge is used to form the linguistic variables to match the process with a controlled variable. The physical normalization and scaling factor can be done by this part. 3. Fuzzy Rules The fuzzy rule based system, which is used to represent the human knowledge, is in the form of natural language expression. The behaviour of the control surface relates the input and output variables of the system, and is governed by a set of rules. Normally, the IF-THEN rule form is preferred. A typical rule would be : where If x is A and y is B, then Z = f(x,y) (5.1) A,B = Fuzzy sets in the antecedent Z= f(x,y) = Crisp function consequence to the input variables x and y 4. Fuzzy Inference Engine When a set of input variables are read, a rule that has any degree of truth in its premise, fires and contributes to the forming of the control surface by approximately modifying it. When all the rules are fired, then the resulting

4 102 control surface is expressed as a fuzzy set, to represent the constraint output. The above operation is termed as Fuzzy Inference Engine. Two types of popular methods used for designing the inference engine of the Fuzzy Logic Controller are: Composition based Interface (Through Max-Min Composition operation) and Rule base matrix. For this work, the Mamdani Fuzzy model is used for the conical tank process. The inference used in this work is the max-min method where the min operation is used for the and conjunction, and max is used for the or conjunction. Thus, the membership function of the input is obtained for each rule. The value is the firing value for each rule. 5. Defuzzification Defuzzification is the block which converts the fuzzy quantity into crisp quantity. The different methods available for defuzzification may be : Maximum membership function method, Weighted average method, First in or Last out of the maximum, Centre of the Largest area, etc. (Thimorthy J. Ross). The most prevalent method is the centroid method, which utilizes the following formula: µ ( ) = µ( ) µ( ) (5.2) where µ=membership degree of the output of x 5.3 DEVELOPMENT OF THE FUZZY LOGIC CONTROLLER To design the Fuzzy Logic Controller (FLC), the variable which can represent the dynamic performance of the plant to be controlled, should

5 103 be chosen as the input to the controller. The Fuzzy Logic Controller has two input variables, namely, the error (e) and the rate of change of error (de), which produces the control signal (cs). The input and output variables are converted into Linguistic variables. This case consists of five fuzzy subsets, labeled as: Negatively Big (NB), Negatively Small (NS), Absolutely Zero (AZ), Positively Small (PS) and Positively Big (PB). Each membership function has two parameters, that may be the centre and the width of the triangular functions Selection of the Triangular Membership function of the Fuzzy Logic Controller For the design of the membership function, normally the triangle is selected. The Justification for selecting the triangular membership function in the Fuzzy Logic Controller is as follows: i. The triangular membership function has a left point, centre point and right point, by which we can calculate the slope of the triangle and obtain the best performance. ii. By changing the overlap (Point of cross-over between successive triangles) and sensitivity (Modify the shape of the membership function), the effect on the performance of the controller is modified. iii. The triangular membership function response produces : satisfactory transient response without any oscillations and also produces minimum settling time. minimum steady state error. speed of response is fast, and similarly, the speed of the overshoot is to be exactly equal to zero.

6 104 The minimum error in the set point changes, and a negligible overshoot at sudden load also changes Membership function of the Fuzzy Logic Controller follows: The membership functions of the Fuzzy Logic Controller are as 1. Membership function of the input variable error For the Fuzzy Logic Controller, the membership function of the input variable error, and the degree of membership function are shown in Figure 5.2. Figure 5.2 Membership function for the input variable error representation in FLC 2. Membership function for the input variable of the rate of change of error For the Fuzzy Logic Controller, the membership function of the input variable of the rate of change of error, and the degree of membership function are shown in Figure 5.3.

7 105 Figure 5.3 Membership functions for the input variable of the rate of change of error representation in FLC 3. Membership function for the Output or Controller variable For the Fuzzy Logic Controller, the membership function of the output variable or control signal, and the degree of membership function are shown in Figure 5.4. Figure 5.4 Membership functions for the output variable representation in FLC

8 Rule base tabulation of the Fuzzy Logic Controller The input error (e) and the rate of change of error (de) combination which produces the change in the control signal (cs) for the fuzzy rule base, is shown in Tabulation 5.1. The tabulation can be obtained, based on five fuzzy Linguistic variables as: Negatively Big (NB), Negatively Small (NS), Absolutely Zero (AZ), Positively Small (PS) and Positively Big (PB). Table 5.1 Rule base tabulation for the Fuzzy Logic Controller de e PB PS AZ NS NB PB PB PB PB PS AZ PS PB PS PS AZ NS AZ PB PS AZ NS NB NS PS AZ NS NS NB NB AZ NS NB NB NB 5.4 SIMULATION RESULTS Servo operation with the Fuzzy Logic Controller In the servo operation, the process with load variable needs to be a constant, and the set point value is a variable. The closed loop servo response for the nonlinear conical tank process is obtained, for the variation of the set point value, using the Fuzzy Logic Controller. For the servo operation, the simulation diagram for the Fuzzy Logic Controller with the height of 20 cm from 0 to 500 Seconds and further the height is increased 10 cm from 500 to 1000 Seconds is shown in Figure 5.5.

9 107 e ISE ITSE ITAE IAE Performance Indices 1 Display 1 de Memory1 Fin Lev el e Fuzzy Logic Controller1 Conical Tank 1 Scope 1 Setpoint Level 1 Setpoint Level 2 Figure 5.5 Simulation diagram of the Fuzzy Logic Controller for the conical tank process in the Servo operation The simulated diagram of the Fuzzy Inference System (FIS), Rule viewer representation and Surface viewer representation of the designed Fuzzy Logic Controller are shown in Figures 5.6, 5.7 and Fuzzy Inference System Editor The Fuzzy Inference System (FIS) editor displays the information about a fuzzy inference system. In the fuzzy inference system, the two inputs represented are: the left side of the inputs are error (e) and rate of change of error (de), and the right side of the output is the control signal (cs). The

10 108 simulated diagram of the Fuzzy Inference System (FIS) for the design of the Fuzzy Logic Controller is shown in Figure 5.6. Figure 5.6 Simulated diagram of the Fuzzy Inference System 2. Rule viewer The rule viewer shows how the shape of certain membership functions influences the overall result. The two inputs variables are : error (e) and the rate of change of error (de), which produce the control signal (cs). The simulated diagram of the rule viewer for the design of the Fuzzy Logic Controller is shown in Figure 5.7.

11 109 Figure 5.7 Simulated rule viewer of the Fuzzy Logic Controller 3. Surface Viewer The surface viewer is a three dimensional curve that represents the mapping. The surface viewer represents the 2-inputs for our application X (error), Y (rate of change of error) and the single output Z (control signal). The simulated diagram of the surface viewer for the design of the Fuzzy Logic Controller is shown in Figure 5.8.

12 110 Figure 5.8 Simulated surface viewer of the Fuzzy Logic Controller The simulated output graphs for Level versus Time, Error signal versus Time and Control signal versus Time are shown in Figures 5.9, 5.10 and 5.11.The control signal magnitude is made consistent through gain adjustment. For the servo operation, the performance index values such as ISE, ITSE, IAE, ITAE, and the Time domain specifications are obtained and tabulated in Table 5.2.

13 111 Figure 5.9 Process variables versus Time graph for the Servo operation of the Fuzzy Logic Controller for the conical tank process with the height of 20 cm and further the height is increased 10 cm Figure 5.10 Error signal versus Time graph for the Servo operation of the Fuzzy Logic Controller for the conical tank process

14 112 Figure 5.11 Control signal versus Time graph for the Servo Operation of the Fuzzy Logic Controller for the Conical tank process Regulatory operation with the Fuzzy Logic Controller In the regulatory operation, the set point value needs to be a constant and the process with the load variable is a variable. The closed loop regulatory response for the nonlinear conical tank process, using the Fuzzy Logic Controller is obtained for the variation of the load. For the regulatory operation, the simulation diagram of the Fuzzy Logic Controller for the height of 30 cm from 0 to 1000 Seconds with +10% Load changes after 800 Seconds, is shown in Figure The simulated output graphs for Level versus Time, Error signal versus Time and Control signal versus Time, are shown in Figures 5.13, 5.14 and For the regulatory operation, the performance index values such as ISE, ITSE, IAE, ITAE, and the Time domain specifications are obtained and tabulated in Table 5.2.

15 113 e ISE ITSE ITAE IAE Perfo rma nce Ind ices 1 Display 1 de M em ory1 Fin e Fuzzy Logic Control ler1 D Lev el Conica l T ank 1 Scope 1 Setpoint Level 1 Disturbance 1 Figure 5.12 Simulation diagram of the Fuzzy Logic Controller for the conical tank process in the Regulatory operation Figure 5.13 Process Level versus Time graph for the Regulatory Operation of the Fuzzy Logic Controller for the Conical tank for the height of 30 cm with +10% Load changes after 800 Seconds

16 114 Figure 5.14 Error signal versus Time graph for the Regulatory operation of the Fuzzy Logic Controller for the Conical tank process Figure 5.15 Control signal versus Time graph for the Regulatory Operation of the Fuzzy Logic Controller for the Conical tank process

17 Servo-Regulatory operation with the Fuzzy Logic Controller In the Servo-regulatory operation, the set point is variable and the process with load variable is also variable. For the variation of the set point value with the load variable changes, the closed loop servo-regulatory response for the nonlinear conical tank process with the Fuzzy Logic Controller is obtained. The simulation diagram of the Fuzzy Logic Controller for height of 20 cm from 0 to 500 Seconds and further height is increased 10 cm from 500 to 1000 Seconds with the load changes of +10% after 800 Seconds is shown in Figure The simulated output graphs for Level versus Time, Error signal versus Time and Control signal versus Time, are shown in Figures 5.17, 5.18 and For the Servo-regulatory operation, the performance index values such as ISE, ITSE, IAE, ITAE, and the Time domain specifications are obtained and tabulated in table 5.1. ISE e ITSE ITAE IAE Performance Indices 1 Display 1 de Memory1 Fin e Fuzzy Logic Controller1 D Lev el Conical T ank 1 Scope 1 Setpoint Level 1 Setpoint Level 2 Disturbance 1 Figure 5.16 Simulation diagram of the Fuzzy Logic Controller for the Conical tank process in the Servo-Regulatory operation

18 116 Figure 5.17 Process variable versus Time graph for the Servo- Regulatory operation of the Fuzzy Logic Controller for the conical tank process with the height of 20 cm and further the height is increased 10 cm with +10% Load changes after 800 Seconds Figure 5.18 Error signal versus Time graph for the Servo-Regulatory operation of the Fuzzy Logic Controller for the conical tank process

19 117 Figure 5.19 Control signal versus Time graph for the Servo- Regulatory operation of the Fuzzy Logic Controller for the conical tank process In Table 5.2 of results, the performance index values, and time domain specifications depend on the dead time or delay of the process. In the present case, the delay time is chosen as 15 Seconds, corresponding to the height of 30 cm of the conical tank. The designed Fuzzy Logic Controller is compared with the Neuro tuned PI Controller for the conical tank process, and operated by the servo tracking, regulatory, and servo-regulatory operations. The controller performance is evaluated by the performance index values of error minimization, and Time domain specification criteria.

20 118 Table 5.2 Comparison of the Performance index values and Time domain specifications of the Fuzzy Logic Controller with the Neuro tuned PI Controller for the conical tank process in the Servo, Regulatory and Servo-Regulatory operations Performance index values and Time domain specifications Regulatory operation Fuzzy Logic Controller Servo Operation Regulatory Operation Servo- Servo- Regulatory operation Neuro tuned PI Controller Servo Operation Regulatory Operation ISE ITSE ITAE IAE Peak overshoot (M p ) Settling time (T s ) 115Sec 110Sec 152Sec 188.5Sec 185 Sec 195.8Sec Steady state error (e ss ) For the servo operation, the Fuzzy Logic Controller and Neuro tuned PI Controller are operated at the height of 20 cm from 0 to 500 Seconds and further the height is increased 10 cm from 500 to 1000 Seconds. In servo operation, the above controller s performance index values such as ISE, ITSE, IAE, ITAE, and the Time domain specifications such as peak over shoot (M p ), settling time (T s ) and steady state error (e ss ) are obtained and tabulated in Table 5.2. From the table, it is observed that the Fuzzy Logic Controller produces better performance values and Time domain specifications, when compared with the Neuro tuned PI Controller. From this, it is concluded that the Fuzzy Logic Controller can produce faster settling response, when compared with the Neuro tuned PI Controller in the servo operation. In the regulatory operation, the Fuzzy Logic Controller and Neuro tuned PI Controller are operated at the height of 30 cm from 0 to 1000 Seconds with +10 % load changes after 800 Seconds. In the regulatory operation, the Fuzzy Logic Controller s performance index values such as

21 119 ISE, ITSE, IAE, ITAE, and the Time domain specifications, such as peak over shoot (M p ), settling time (T s ) and steady state error (e ss ) are obtained and tabulated in Table 5.2. From the table, it is observed that the Fuzzy Logic Controller produces better performance values and Time domain specifications, when compared with the Neuro tuned PI Controller. From this, it is concluded that the Fuzzy Logic Controller can produce faster settling response when compared with the Neuro tuned PI Controller in regulatory operation. Similarly, in the servo-regulatory operation, the Fuzzy Logic Controller and Neuro tuned PI Controller are operated at the height of 20 cm from 0 to 500 Seconds and further the height is increased 10 cm from 500 Seconds to 1000 Seconds with +10% Load changes after 800 Seconds. In the servo-regulatory operation, the above controller s performance index values such as ISE, ITSE, IAE, ITAE, and the Time domain specifications, such as peak over shoot (M p ), settling time (T s ) and steady state error (e ss ) are obtained, and tabulated in Table 5.2. From the table, it is observed that the Fuzzy Logic Controller produces better performance values and Time domain specifications when compared with the Neuro tuned PI Controller. From this, it is concluded that the Fuzzy Logic Controller can produce faster settling response, when compared with the Neuro tuned PI Controller in the servoregulatory operation. From the servo, regulatory and servo-regulatory operations, it is seen that the Fuzzy Logic Controller is able to produce faster response with minimum error, and is also preferable for the conical tank process, when compared with the Neuro tuned PI Controller. Further, Ganesh Ram et al. (2012) developed the Adaptive tuned Fuzzy PI Controller for conical tank process. But in this work, the Fuzzy Logic Controller for Conical tank process is developed, in which this

22 120 controller response produces a settling time and an error less than the one reported in Ganesh Ram et al. (2012). This shows that the developed Fuzzy Logic Controller performs better than the adaptive tuned Fuzzy PI controller of Ganesh Ram et al. 5.5 SUMMARY AND CONCLUSION This chapter presented the design of the Fuzzy Logic Controller for the conical tank level process. The simulation results are obtained for this controller by adjusting the set point changes, load changes and change in set point with load changes. It shows that the response oscillations are reduced for the set point variations, load variations and change in set point with load variations for the Fuzzy Logic Controller as against the Neuro tuned PI Controller. From the graph, it is seen that the Fuzzy Logic Controller produces the minimum peak over shoot and faster settling time. For the above changes in the set point, load changes and change in setpoint with load changes, the Fuzzy Logic Controller is able to produce a faster response when compared to the Neuro tuned PI Controller, Genetic Algorithm tuned PI Controller and Conventional PI Controller. The Fuzzy Logic Controller produces less error and better performance index values, viz., ISE, ITSE, IAE, ITAE. The Fuzzy Logic Controller of the conical tank process also produces better time domain specifications, when compared to the Neuro tuned PI Controller, Genetic Algorithm tuned PI Controller and Conventional PI Controller. The designed controller s performance is also tested in another non-linear process, called the ph process, and the same is discussed in the next Chapter.

### DESIGN OF TUNING METHODS OF PID CONTROLLER USING FUZZY LOGIC

DESIGN OF TUNING METHODS OF PID CONTROLLER USING FUZZY LOGIC Venugopal P, 4-1994/4d, durganagar colony,chittoor, 9500359576, Ajanta Ganguly,95/2/1/1,Abinash Banerjee Lane,shibpur,Howrah-711102,West Bengal,

### Implementation of Fuzzy and PID Controller to Water Level System using LabView

Implementation of Fuzzy and PID Controller to Water Level System using LabView Laith Abed Sabri, Ph.D University of Baghdad AL-Khwarizmi college of Engineering Hussein Ahmed AL-Mshat University of Baghdad

### Matlab simulation of temperature control of heat exchanger using different controllers

Automation, Control and Intelligent Systems 2014; 2(1): 1-5 ublished online March 10, 2014 (http://www.sciencepublishinggroup.com/j/acis) doi: 10.11648/j.acis.20140201.11 Matlab simulation of temperature

### Comparison of Fuzzy PID Controller with Conventional PID Controller in Controlling the Speed of a Brushless DC Motor

Comparison of Fuzzy PID Controller with Conventional PID Controller in Controlling the Speed of a Brushless DC Motor S. Sunisith 1, Lizi Joseph 2, M. Saritha 3 sunisith@gmail.com, lizialex06@gmail.com,

### Performance analysis of FL, PI and PID controller for AGC and AVR of a Two-Area Power System

International Journal of Scientific and Research Publications, Volume 5, Issue 1, January 2015 1 Performance analysis of FL, PI and PID controller for AGC and AVR of a Two-Area Power System Chandrashekar.M.J

### A Fuzzy Logic Load-Frequency Controller for Power Systems

A Fuzzy Logic LoadFrequency Controller for Power Systems İsmail H. Altaş and Jelle Neyens 2 Department Of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey ihaltas@altas.org

### Problems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment,

Uncertainty Problems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment, E.g., loss of sensory information such as vision Incorrectness in

### Introduction to Fuzzy Control

Introduction to Fuzzy Control Marcelo Godoy Simoes Colorado School of Mines Engineering Division 1610 Illinois Street Golden, Colorado 80401-1887 USA Abstract In the last few years the applications of

### JAVA FUZZY LOGIC TOOLBOX FOR INDUSTRIAL PROCESS CONTROL

JAVA FUZZY LOGIC TOOLBOX FOR INDUSTRIAL PROCESS CONTROL Bruno Sielly J. Costa, Clauber G. Bezerra, Luiz Affonso H. G. de Oliveira Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Norte

### Stabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller

Stabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller Nourallah Ghaeminezhad Collage Of Automation Engineering Nuaa Nanjing China Wang Daobo Collage Of Automation Engineering Nuaa Nanjing

### Defuzzification. Convert fuzzy grade to Crisp output. *Fuzzy Engineering, Bart Kosko

Defuzzification Convert fuzzy grade to Crisp output *Fuzzy Engineering, Bart Kosko Defuzzification (Cont.) Centroid Method: the most prevalent and physically appealing of all the defuzzification methods

### TO CONTROL THE CHARACTERISTICS OF AC MOTOR USING FUZZY LOGIC CONTROLLER

TO CONTROL THE CHARACTERISTICS OF AC MOTOR USING FUZZY LOGIC CONTROLLER Sandeep Goyat 1, Dr.Meena Tushir 2 1 Electrical Department, Research Scholar JJT University, Rajasthan, (India). 2 Electrical Department,

### Performance Evaluation of Membership Functions on Fuzzy Logic Controlled AC Voltage Controller for Speed Control of Induction Motor Drive

Performance Evaluation of Membership Functions on Fuzzy Logic Controlled AC Voltage Controller for Speed Control of Induction Motor Drive J. Gayathri Monicka Dr. N.O.Guna Sekhar Bharath University, Chenni

### A FUZZY LOGIC APPROACH FOR SALES FORECASTING

A FUZZY LOGIC APPROACH FOR SALES FORECASTING ABSTRACT Sales forecasting proved to be very important in marketing where managers need to learn from historical data. Many methods have become available for

### EMPLOYEE PERFORMANCE APPRAISAL SYSTEM USING FUZZY LOGIC

EMPLOYEE PERFORMANCE APPRAISAL SYSTEM USING FUZZY LOGIC ABSTRACT Adnan Shaout* and Mohamed Khalid Yousif** *The Department of Electrical and Computer Engineering The University of Michigan Dearborn, MI,

### Introduction to Fuzzy Logic using MATLAB

S.N. Sivanandam, S. Sumathi and S.N. Deepa Introduction to Fuzzy Logic using MATLAB With 304 Figures and 37 Tables &j Springer Contents 1 Introduction 1 1.1 Fuzzy Logic 1 1.2 Mat LAB - An Overview 6 2

### Genetic Tuning of Fuzzy Inference System for Furnace Temperature Controller

Genetic Tuning of Fuzzy Inference System for Furnace Controller Rachana R. Mudholkar, Umesh V. Somnatti P. G. Department of Computer Science and Engineering Dr. M. S. Sheshgiri College of Engineering and

### Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR

International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:5, No:, 20 Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR Saeed

### A Hybrid PSO_Fuzzy_PID Controller for Gas Turbine Speed Control

A Hybrid PSO_Fuzzy_PID Controller for Gas Turbine Speed Control Azadeh Mansouri Mansourabad 1, Mohammad Taghi Hamidi Beheshti 2 and Mohsen Simab 1 Department of Technical and Engineering, International

### Space Vector Pulse Width Modulation Based Speed Control of Induction Motor using Fuzzy PI Controller

Space Vector Pulse Width Modulation Based Speed Control of Induction Motor using Fuzzy PI Controller R. Arulmozhiyal, Member, IEEE, K. Baskaran, Member, IEEE Abstract This paper presents design and implements

### MULTI-OBJECTIVE GENETIC OPTIMISATION FOR SELF-ORGANISING FUZZY LOGIC CONTROL

MULTI-OBJECTIVE GENETIC OPTIMISATION FOR SELF-ORGANISING FUZZY LOGIC CONTROL M.F. Abbod, M. Mahfouf and D.A. Linkens University of Sheffield, Sheffield, UK A multi-objective genetic algorithm is developed

### FPGA Implementation of Three-Phase Induction Motor Speed Control Using Fuzzy Logic and Logic Based PWM Technique

FPGA Implementation of Three-Phase Induction Motor Speed Control Using Fuzzy Logic and Logic Based PWM Technique R.P. Dhobale Prof. D.M. Chandwadkar Abstract This paper presents the design and implementation

### A Fuzzy-Based Speed Control of DC Motor Using Combined Armature Voltage and Field Current

3rd IFAC International Conference on Intelligent Control and Automation Science. A Fuzzy-Based Speed Control of DC Motor Using Combined Armature Voltage and Field Current A. A. Sadiq* G. A. Bakare* E.

### Sliding Mode Fuzzy Logic Control of an Unstable Bioreactor

A publication of 113 CHEMICAL ENGINEERING TRANSACTIONS VOL. 3, 013 Chief Editors: Sauro Pierucci, Jiří J. Klemeš Copyright 013, AIDIC Servizi S.r.l., ISBN 978-88-95608-3-5; ISSN 1974-9791 The Italian Association

### Advanced Controllers Using Fuzzy Logic Controller (FLC) for Performance Improvement

Advanced Controllers Using Fuzzy Logic Controller (FLC) for Performance Improvement Kapil Dev Sharma 1, M. Ayyub 2, Sumit Saroha 3, Ahmad Faras 4 Department of Electrical Engineering, AMU Aligarh, India

### Fuzzy Logic Derivation and Simulation of a Three-Variable Solar Water Heater Using Matlab Fuzzy Logic Toolbox

Fuzzy Logic Derivation and Simulation of a Three-Variable Solar Water Heater Using Matlab Fuzzy Logic Toolbox Rionel Belen Caldo Faculty Member and Research Coordinator, College of Engineering and Computer

### FUZZY LOGIC CONTROLLER FOR PHOTOVOLTAIC ARRAY SIMULATOR

FUZZY LOGIC CONTROLLER FOR PHOTOVOLTAIC ARRAY SIMULATOR R.Raja 1, L.Udhaya kumar 2, S.Rakesh kumar 3 School of Electrical and Electronics Engineering, SASTRA university, Tirumalaisamudram, Thanjavur-613401,

### Project Management Efficiency A Fuzzy Logic Approach

Project Management Efficiency A Fuzzy Logic Approach Vinay Kumar Nassa, Sri Krishan Yadav Abstract Fuzzy logic is a relatively new technique for solving engineering control problems. This technique can

### Design and Analysis of Speed Control Using Hybrid PID-Fuzzy Controller for Induction Motors

Western Michigan University ScholarWorks at WMU Master's Theses Graduate College 6-2015 Design and Analysis of Speed Control Using Hybrid PID-Fuzzy Controller for Induction Motors Ahmed Fattah Western

### MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION IN TRAFFIC SYSTEM BASED ON GENETIC ALGORITHM. Received February 2011; revised June 2011

International Journal of Innovative Computing, Information and Control ICIC International c 2012 ISSN 1349-4198 Volume 8, Number 5(A), May 2012 pp. 3387 3397 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION

### Real time MATLAB Interface for speed control of Induction motor drive using dspic 30F4011

Real time MATLAB Interface for speed control of Induction motor drive using dspic 30F4011 R. Arulmozhiyal Senior Lecturer, Sona College of Technology, Salem, TamilNadu, India. K. Baskaran Assistant Professor,

### Stabilization of Frequency Deviation in an AC-DC Interconnected Power Systems Using Supervisory Fuzzy Controller

Tamkang Journal of Science and Engineering, Vol. 14, No. 4, pp. 341 349 (2011) 341 Stabilization of Frequency Deviation in an AC-DC Interconnected Power Systems Using Supervisory Fuzzy Controller S. Ramesh

### Fuzzy Logic Control Implementation of Rectilinear Plant with Inverted Pendulum

Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 Fuzzy Logic Control Implementation of Rectilinear Plant with Inverted Pendulum

### Detection of DDoS Attack Scheme

Chapter 4 Detection of DDoS Attac Scheme In IEEE 802.15.4 low rate wireless personal area networ, a distributed denial of service attac can be launched by one of three adversary types, namely, jamming

### Fuzzy Logic based Reactive Power Control System in Radial Feeder

Fuzzy Logic based Reactive Power Control System in Radial Feeder Bijaya Lubanjar Pursuing B.E. Degree in Electrical Chandra Khatri Received B.E. Degree in Electrical Pradip Neupane Received B.E. Degree

### Chapter 2 Introduction

Chapter 2 Introduction Preface As part of the Fuzzy Inference System [?], the Matlab environment offers some reliable tools, which allow characterising fuzzy problems. These include: The FIS (Fuzzy Inference

### Fuzzy Set Theory : Soft Computing Course Lecture 29 34, notes, slides RC Chakraborty, Aug.

Fuzzy Set Theory : Soft Computing Course Lecture 29 34, notes, slides www.myreaders.info/, RC Chakraborty, e-mail rcchak@gmail.com, Aug. 10, 2010 http://www.myreaders.info/html/soft_computing.html www.myreaders.info

### FIRE AND SMOKE DETECTION WITHOUT SENSORS: IMAGE PROCESSING BASED APPROACH

FIRE AND SMOKE DETECTION WITHOUT SENSORS: IMAGE PROCESSING BASED APPROACH Turgay Çelik, Hüseyin Özkaramanlı, and Hasan Demirel Electrical and Electronic Engineering, Eastern Mediterranean University Gazimağusa,

### A Fuzzy Controller for Blood Glucose-Insulin System

Journal of Signal and Information Processing, 213, 4, 111-117 http://dx.doi.org/1.4236/jsip.213.4215 Published Online May 213 (http://www.scirp.org/journal/jsip) 111 Ahmed Y. Ben Sasi 1, Mahmud A. Elmalki

### Saumil Navalbhai Patel B.E., Gujarat University, India, 2007 PROJECT. Submitted in partial satisfaction of the requirements for the degree of

POWER LOAD BALANCING USING FUZZY LOGIC Saumil Navalbhai Patel B.E., Gujarat University, India, 2007 PROJECT Submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in

### Interactive Fuzzy Interval Reasoning for smart Web shopping

Applied Soft Computing 5 (2005) 433 439 www.elsevier.com/locate/asoc Interactive Fuzzy Interval Reasoning for smart Web shopping Fuyu Liu, Hongli Geng, Yan-Qing Zhang* Department of Computer Science, Georgia

### FUZZY LOGIC SYSTEMS. 1. Why Fuzzy Logic? Environmental Control Air Conditioners Humidifiers

FUZZY LOGIC SYSTEMS James Vernon: Visiting Consultant Scientist, control systems principles.co.uk ABSTRACT: This is one of a series of white papers on systems modelling, analysis and control, prepared

### MODELING AND SIMULATION OF STATIC VAR COMPENSATOR FUZZY CONTROL FOR POWER SYSTEM STABILITY ENHANCEMENT

The 6 th edition of the Interdisciplinarity in Engineering International Conference Petru Maior University of Tîrgu Mureş, Romania, 202 MODELING AND SIMULATION OF STATIC VAR COMPENSATOR FUZZY CONTROL FOR

### PetraFuz: a Low Cost Embedded Controller Based Fuzzy Logic Development System

PetraFuz: a Low Cost Embedded Controller Based Fuzzy Logic Development System Thiang, Anies Hannawati, Resmana Lim and Hany Ferdinando Electrical Engineering Department, Petra Christian University Siwalankerto

### Survey paper. Adaptive Fuzzy Logic Controllers for DC Drives: A Survey of the State of the art

E. E. El-kholy A. M. Dabroom General Organization for Technical Education and Vocational Training, College of Telecom. & Electronics, R & D Center, Jeddah, Saudi Arabia. eelkholy@yahoo.com amdabroom@yahoo.com

### Fuzzy Priority CPU Scheduling Algorithm

www.ijcsi.org 386 Fuzzy CPU Scheduling Algorithm Bashir Alam 1, M.N. Doja 1, R. Biswas 3, M. Alam 4 1 Department of Computer Engineering, Jamia Millia Islamia 2 Department of Computer Engineering, Jamia

### Another fuzzy membership function that is often used to represent vague, linguistic terms is the Gaussian which is given by:

Gaussian Membership Functions Another fuzzy membership function that is often used to represent vague, linguistic terms is the Gaussian which is given by: µ A i(x) = exp( (c i x) 2 ), (1) 2σ 2 i where

### DIAGNOSIS OF THE JAUNDICE USING FUZZY EXPERT SYSTEM

DIAGNOSIS OF THE JAUNDICE USING FUZZY EXPERT SYSTEM Nitin Sahai 1,Deepshikha Shrivastava 2, Pankaj Srivastava 3 1 Department of Biomedical Engineering, North Eastern Hill University, Shillong 2 Department

### EECE 460 : Control System Design

EECE 460 : Control System Design PID Controller Design and Tuning Guy A. Dumont UBC EECE January 2012 Guy A. Dumont (UBC EECE) EECE 460 PID Tuning January 2012 1 / 37 Contents 1 Introduction 2 Control

### PROBABILISTIC AND FUZZY FAULT-TREE ANALYSES FOR MODELLING CAVE-IN ACCIDENTS

H.M. Al-Humaidi, Int. J. of Safety and Security Eng., Vol. 3, No. 3 (2013) 165 173 PROBABILISTIC AND FUZZY FAULT-TREE ANALYSES FOR MODELLING CAVE-IN ACCIDENTS H.M. AL-HUMAIDI Kuwait University, Kuwait.

### METHOD OF COMPUTATIONAL INTELLIGENCE IN POWER ELECTRONICS

METHOD OF COMPUTATIONAL INTELLIGENCE IN POWER ELECTRONICS Adriana FLORESCU, Dan Alexandru STOICHESCU, Dumitru STANCIU University Politehnica of Bucharest, Faculty of Electronics and Telecommunications,

### PID Controlled Automatic Voltage Regulator with Load Frequency Control

I J E E E C International Journal of Electrical, Electronics ISSN No. (Online): 2277-2626 and Computer Engineering 5(2): 05-10(2016) PID Controlled Automatic Voltage Regulator with Load Frequency Control

### Dynamic Simulation of Induction Motor Drive using Neuro Controller

Int. J. on Recent Trends in Engineering and Technology, Vol. 1, No. 2, Jan 214 Dynamic Simulation of Induction Motor Drive using Neuro Controller P. M. Menghal 1, A. Jaya Laxmi 2, N.Mukhesh 3 1 Faculty

### IMPLEMENTING FUZZY LOGIC IN DETERMINING SELLING PRICE

IMPLEMENTING FUZZY LOGIC IN DETERMINING SELLING PRICE (Danny Prabowo Soetanto) IMPLEMENTING FUZZY LOGIC IN DETERMINING SELLING PRICE Danny Prabowo Soetanto Dosen Fakultas Teknik Jurusan Teknik Industri

### stable response to load disturbances, e.g., an exothermic reaction.

C REACTOR TEMPERATURE control typically is very important to product quality, production rate and operating costs. With continuous reactors, the usual objectives are to: hold temperature within a certain

### An Investigation Of Productivity In Boilers Of Thermal Power Plants With Fuzzy Gain Scheduled PI Controller

Int.J.Eng.Research & Development,Vol.2,No.1,January 2010 45 An Investigation Of Productivity In Boilers Of Thermal Plants With Fuzzy Gain Scheduled PI Controller İlhan KOCAARSLAN 1, Ertuğrul ÇAM 2, Hasan

### Fuzzy Logic Operation Control for PV-Diesel-Battery Hybrid Energy System

70 The Open Renewable Energy Journal, 2009, 2, 70-78 Open Access Fuzzy Logic Operation Control for PV-Diesel-Battery Hybrid Energy System Abd El-Shafy A. Nafeh* Electronics Research Institute, Cairo, Egypt

### Deferring Elimination of Design Alternatives in Object- Oriented Methods

Deferring Elimination of Design Alternatives in Object- Oriented Methods Mehmet Aksit and Francesco Marcelloni TRESE project, Department of Computer Science, University of Twente, P.O. Box 217, 7500 AE

### CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVII - Fuzzy Control Systems - Jens Jäkel, Ralf Mikut and Georg Bretthauer

FUZZY CONTROL SYSTEMS Jens Jäkel, Ralf Mikut and Institute of Applied Computer Science, Forschungszentrum Karlsruhe GmbH, Germany Keywords: Adaptation, defuzzification, expert knowledge, fuzzification,

### A FUZZY PI CONTROLLER APPLICATION IN BOILERS OF THERMAL POWER PLANTS

A FUZZY PI CONTROLLER APPLICATION IN BOILERS OF THERMAL POWER PLANTS İlhan Kocaarslan 1 Ertuğrul Çam 2 Hasan Tiryaki 3 M. Cengiz Taplamacıoğlu 4 1,2,3 Kirikkale University, Department of Electrical & Electronics

### A Learning Fuzzy System for Looper Control in Rolling Mills

A Learning Fuzzy System for Looper Control in Rolling Mills F. Janabi-Sharifi J. Fan Department of Mechanical Engineering Quad Engineering Inc. Ryerson Polytechnic University North Yor, Ontario, Canada

### Object Following Fuzzy Controller for a Mobile Robot

Copyright 2012 American Scientific Publishers All rights reserved Printed in the United States of America Journal of Computational Intelligence and Electronic Systems Vol. 1, 1 5, 2012 Irfan Ullah 1, Furqan

### Water Resources Research Report

THE UNIVERSITY OF WESTERN ONTARIO DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING Water Resources Research Report Water Resources Decision Making Under Uncertainty By: Dejan Vucetic and S. P. Simonovic

### A simple method to determine control valve performance and its impacts on control loop performance

A simple method to determine control valve performance and its impacts on control loop performance Keywords Michel Ruel p.eng., Top Control Inc. Process optimization, tuning, stiction, hysteresis, backlash,

### Fast Fuzzy Control of Warranty Claims System

Journal of Information Processing Systems, Vol.6, No.2, June 2010 DOI : 10.3745/JIPS.2010.6.2.209 Fast Fuzzy Control of Warranty Claims System Sang-Hyun Lee*, Sung Eui Cho* and Kyung-li Moon** Abstract

### Genetic Optimization Tuning of an Automatic Voltage Regulator System

Genetic Optimization Tuning of an Automatic Voltage Regulator System Sapna Bhati, Dhiiraj Nitnawwre Depart,ent of Electronics and Telecommunication, IET-DAVV Indore M.P. India Sapna_cit@yahoo.com, dheerajnitnaware@gmail.com

### DESIGN OF WATER LEVEL CONTROLLER USING FUZZY LOGIC SYSTEM

DESIGN OF WATER LEVEL CONTROLLER USING FUZZY LOGIC SYSTEM Thesis submitted in partial fulfilment of the requirements for the degree of Bachelor of Technology (B. Tech) In Mechanical Engineering By Harshdeep

### 1733 P a g e. Keywords: PI, Fuzzy controller, DVR, Voltage sags, Voltage swells.

(IJERA) ISS: 2248-9622 www.ijera.com Mitigation Of Voltage Sags/Swells By Dynamic Voltage Restorer Using i And Fuzzy Logic Controller CH SRISAILAM 1,A SREEIVAS 2 Asst.rofessors, Department Of Electrical

### Computational Intelligence Introduction

Computational Intelligence Introduction Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2011 Farzaneh Abdollahi Neural Networks 1/21 Fuzzy Systems What are

### Modeling and Simulation of Fuzzy Logic Variable Speed Drive Controller

Chapter 4 Modeling and Simulation of Fuzzy Logic Variable Speed Drive Controller 4.1 Introduction Fuzzy logic is an important part of artificial intelligence. In recent times, artificial intelligence techniques

### FUZZY ROUND ROBIN CPU SCHEDULING ALGORITHM

Journal of Computer Science 9 (8): 1079-1085, 2013 ISSN: 1549-3636 2013 doi:10.3844/jcssp.2013.1079.1085 Published Online 9 (8) 2013 (http://www.thescipub.com/jcs.toc) FUZZY ROUND ROBIN CPU SCHEDULING

### A FUZZY MATHEMATICAL MODEL FOR PEFORMANCE TESTING IN CLOUD COMPUTING USING USER DEFINED PARAMETERS

A FUZZY MATHEMATICAL MODEL FOR PEFORMANCE TESTING IN CLOUD COMPUTING USING USER DEFINED PARAMETERS A.Vanitha Katherine (1) and K.Alagarsamy (2 ) 1 Department of Master of Computer Applications, PSNA College

### Y(s) U(s) The continuous process transfer function is denoted by G: (Eq.4.40)

The Process PID control tuner provides the open and closed loop process system responses for a continuous process model (G) with a continuous PID controller (Gc). The Process model can be characterized

### Fuzzy Logic Based Reactivity Control in Nuclear Power Plants

Fuzzy Logic Based Reactivity Control in Nuclear Power Plants Narrendar.R.C 1, Tilak 2 P.G. Student, Department of Mechatronics Engineering, VIT University, Vellore, India 1 P.G. Student, Department of

### Review of some concepts in predictive modeling

Review of some concepts in predictive modeling Brigham and Women s Hospital Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support A disjoint list of topics? Naïve Bayes

### Artificial Intelligence: Fuzzy Logic Explained

Artificial Intelligence: Fuzzy Logic Explained Fuzzy logic for most of us: It s not as fuzzy as you might think and has been working quietly behind the scenes for years. Fuzzy logic is a rulebased system

### Adaptive Optimal Scheduling of Public Utility Buses in Metro Manila Using Fuzzy Logic Controller

Adaptive Optimal Scheduling of Public Utility Buses in Metro Manila Using Fuzzy Logic Controller Cyrill O. Escolano a*, Elmer P. Dadios a, and Alexis D. Fillone a a Gokongwei College of Engineering De

### MARCIN DETYNIECKI LIP6 - CNRS -University of Paris VI 4, place Jussieu Paris, France

vol. 8 (5), pp. 573-592, 2. RANKING FUZZY NUMBERS USING -WEIGHTED VALUATIONS MARCIN DETYNIECKI LIP6 - CNRS -University of Paris VI 4, place Jussieu 755 Paris, France Marcin.Detyniecki@lip6.fr RONALD R.

### Brushless DC Motor Speed Control using both PI Controller and Fuzzy PI Controller

Brushless DC Motor Speed Control using both PI Controller and Fuzzy PI Controller Ahmed M. Ahmed MSc Student at Computers and Systems Engineering Mohamed S. Elksasy Assist. Prof at Computers and Systems

### -

FUZZY HRRN CPU SCHEDULING ALGORITHM 1 Bashir Alam, 1 M.N. Doja, 2 R. Biswas, 3 M. Alam 1 Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India 2 Department of Computer Science and

### Process Control & Instrumentation part IV Control loops

This chapter aims to bring basic knowledge of control strategies in HVAC systems through a real case study: Temperature control of an open office. Objectives: At the end of this course, you should be able

### D A T A M I N I N G C L A S S I F I C A T I O N

D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.

### Fuzzy Implication Rules. Adnan Yazıcı Dept. of Computer Engineering, Middle East Technical University Ankara/Turkey

Fuzzy Implication Rules Adnan Yazıcı Dept. of Computer Engineering, Middle East Technical University Ankara/Turkey Fuzzy If-Then Rules Remember: The difference between the semantics of fuzzy mapping rules

### Fuzzy Classification of Human Skin Color in Color Images

Fuzzy Classification of Human Skin Color in Color Images I. A. G. Boaventura, V. M. Volpe, I. N. da Silva, A. Gonzaga Abstract In this paper a fuzzy approach for the classification of skin color tones

### Conception and Development of a Health Care Risk Management System

2011 International Conference on Biomedical Engineering and Technology IPCBEE vol.11 (2011) (2011) IACSIT Press, Singapore Conception and Development of a Health Care Risk Management System Nesrine Zoghlami,

### Fuzzy decision trees

Fuzzy decision trees Catalin Pol Abstract Decision trees are arguably one of the most popular choices for learning and reasoning systems, especially when it comes to learning from discrete valued (feature

### Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk

BMAS 2005 VHDL-AMS based genetic optimization of a fuzzy logic controller for automotive active suspension systems Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk Outline Introduction and system

### Models for Inexact Reasoning. Fuzzy Logic Lesson 1 Crisp and Fuzzy Sets. Master in Computational Logic Department of Artificial Intelligence

Models for Inexact Reasoning Fuzzy Logic Lesson 1 Crisp and Fuzzy Sets Master in Computational Logic Department of Artificial Intelligence Origins and Evolution of Fuzzy Logic Origin: Fuzzy Sets Theory

### Simulation of VSI-Fed Variable Speed Drive Using PI-Fuzzy based SVM-DTC Technique

Simulation of VSI-Fed Variable Speed Drive Using PI-Fuzzy based SVM-DTC Technique B.Hemanth Kumar 1, Dr.G.V.Marutheshwar 2 PG Student,EEE S.V. College of Engineering Tirupati Senior Professor,EEE dept.

### RISK ASSESSMENT BASED UPON FUZZY SET THEORY

RISK ASSESSMENT BASED UPON FUZZY SET THEORY László POKORÁDI, professor, University of Debrecen pokoradi@mfk.unideb.hu KEYWORDS: risk management; risk assessment; fuzzy set theory; reliability. Abstract:

### Real Time Traffic Balancing in Cellular Network by Multi- Criteria Handoff Algorithm Using Fuzzy Logic

Real Time Traffic Balancing in Cellular Network by Multi- Criteria Handoff Algorithm Using Fuzzy Logic Solomon.T.Girma 1, Dominic B. O. Konditi 2, Edward N. Ndungu 3 1 Department of Electrical Engineering,

### Time Response Analysis of DC Motor using Armature Control Method and Its Performance Improvement using PID Controller

Available online www.ejaet.com European Journal of Advances in Engineering and Technology, 5, (6): 56-6 Research Article ISSN: 394-658X Time Response Analysis of DC Motor using Armature Control Method

### Control of the Heating System with Fuzzy Logic

World Applied Sciences Journal 23 (11): 1441-1447, 2013 ISSN 1818-4952 IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.23.11.13156 Control of the Heating System with Fuzzy Logic Iskandar Souleiman

### ABSTRACT. Keyword double rotary inverted pendulum, fuzzy logic controller, nonlinear system, LQR, MATLAB software 1 PREFACE

DESIGN OF FUZZY LOGIC CONTROLLER FOR DOUBLE ROTARY INVERTED PENDULUM Dyah Arini, Dr.-Ing. Ir. Yul Y. Nazaruddin, M.Sc.DIC, Dr. Ir. M. Rohmanuddin, MT. Physics Engineering Department Institut Teknologi

### DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM

DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM ARSHDEEP KAUR University College of Engineering, Punjabi University, Patiala, Punjab- 147002, India arshdeep_24@yahoo.in AMRIT

### Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms

IJCSNS International Journal of Computer Science and Network Security, VOL.8 No., February 8 7 Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms Y.Dhanalakshmi and Dr.I. Ramesh

### FS I: Fuzzy Sets and Fuzzy Logic. L.A.Zadeh, Fuzzy Sets, Information and Control, 8(1965)

FS I: Fuzzy Sets and Fuzzy Logic Fuzzy sets were introduced by Zadeh in 1965 to represent/manipulate data and information possessing nonstatistical uncertainties. L.A.Zadeh, Fuzzy Sets, Information and

### A Fuzzy Logic Based Approach for Selecting the Software Development Methodologies Based on Factors Affecting the Development Strategies

Available online www.ejaet.com European Journal of Advances in Engineering and Technology, 2015, 2(7): 70-75 Research Article ISSN: 2394-658X A Fuzzy Logic Based Approach for Selecting the Software Development