FUZZY SYSTEMS

Paper Code: 
MCS 426 B
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course introduces the basic concepts of fuzzy set theory and analyze several application case studies, with a special emphasis on the design, tuning, and deployment of Fuzzy Logic Controllers (FLC) and other fuzzy systems.

12.00
Unit I: 
Introduction to Neural Networks

Introduction, Humans and Computers, Organization of the Brain. General Description:  Fuzzy Set and Fuzzy Logic: motivation, possibilistic interpretation, basic concepts.

12.00
Unit II: 
Classical & Fuzzy Sets

Introduction to classical sets - properties, Operations and relations; Fuzzy sets, Membership, Uncertainty, Operations, properties, fuzzy relations and inferences, cardinalities, membership functions.

12.00
Unit III: 
Fuzzy Logic System Components

Fuzzification, Membership value assignment, development of rule base and decision making system, Defuzzification to crisp sets, Defuzzification methods.

12.00
Unit IV: 
Fuzzy Logic Control

Membership function-knowledgebase, decision making logic- optimization of membership function using neural network- Adaptive Fuzzy system, Introduction to genetic algorithm.
 

12.00
Unit V: 
Applications

Fuzzy Logic Applications:  approximate reasoning, fuzzy arithmetic, linguistic models, decision theory, classification, and fuzzy controllers (development, tuning, compilation, deployment).  Fuzzy logic control and Fuzzy classification

Computational Intelligence (CI): hybrid systems based on fuzzy.
 

ESSENTIAL READINGS: 
  1. H.J.Zimmermann, ”fuzzy set theory and its applications”, Allied publication, Ltd1996
  2. Johy Yen & Reza Langari, “ Fuzzy Logic:- Intelligence control and information”, pearson education, new delhi,2003


 

REFERENCES: 
  1. I.S.R. Jang, c.t.sun, E.mizutani, “Neuro-Fuzzy and soft computing”, prentice hall, 1997
  2. Timothy J.Rose, “ Fuzzy Logic with Engineering Applications”, Tata McGraw Hill, 1997.
     
Academic Year: 
2017-18
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