M.Sc. CSIT Syllabus

Fuzzy Systems

Course Title: Fuzzy Systems
Full Marks: 45 + 30
Course No: C.Sc. 621
Pass Marks: 22.5 + 15
Nature of the Course: Theory + Lab
Credit Hrs: 3

Course Description:

This course.deals with introduction, fuzzy mapping, membership functions, fuzzy knowledge based system, fuzzy controller, nonlinear systems and adaptive fuzzy controller, and hybrid systems.

Course Objectives:

  • Introduce the concept of fuzzy logic.
  • Design using fuzzy system.

Unit 1: Introduction to fuzzy set theory: 7Hrs

1.1 Probabilistic reasoning,
1.2 Fuzzy sets,
1.3 mathematics of fuzzy set theory,
1.4 operations on fuzzy sets,
1.5 comparison of fuzzy and crisp set theory.

Unit 2: Fuzzy mapping: 7Hrs

2.1 one to one mapping,
2.2 max-min principle,
2.3 extension principle,
2.4 implication rules – mamdani implications.

Unit 3: Membership functions: 8Hrs

3.1 Universe of discourse,
3.2 mapping inside fuzzy domain,
3.3 fuzzy membership mapping methods,
3.4 application to real world problems.

Unit 4: Fuzzy knowledge based systems: 7Hrs

4.1 Fuzzification,
4.2 Fuzzy knowledge base, rule base,
4.3 Data base for fuzzy,
4.4 Inference rules,
4.5 defuzzyfication methods of defuzzification.

Unit 5: Fuzzy controller: 6Hrs

5.1 Control strategies,
5.2 general PID controller,
5.3 Implementation of fuzzy systems in control ,
5.4 Direct fuzzy controller,
5.5 PI and PID controller,
5.6 Indirect fuzzy controller – fuzzy in handling the inner loops of control systems.

Unit 6: Nonlinear systems and adaptive fuzzy controller:6Hrs

6.1 Nonlinear systems,
6.2 modification in fuzzy systems for nonlinear control,
6.3 Adaptive control,
6.4 Adaptive control using fuzzy,
6.5 fuzzy sliding mode controls.

Unit 7: Hybrid systems: 4Hrs

7.1 Neuro- fuzzy and fuzzy genetic systems,
7.2 Applications to scientific problems.


The laboratory exercises should contain all the features mentioned above.

Reference books:

1.Neural Networks and Fuzzy Logics, Bart Kosko, Pearson Education, NewDelhi, 2011 2.Fuzzy logic to engineering applications –Timothy J. Ross.
3.Fuzzy control –Drianlcov.
4.Fuzzy modeling and control –Yager.

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Prince Pudasaini