JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objective:

• 1. To cater the knowledge of neural networks and fuzzy logic Control and use these for controlling real time systems.
• 2. To provide knowledge of adaptive fuzzy system.
• 3. To learn the concept of artificial neural networks.
• 4. To apply mapping and recurrent networks.
• 5. To know the cases studies of neural networks.

Course Outcomes

At the end of the course, the student will be able to:

• 1. Expose the basic concepts of fuzzy logic control
• 2. Understand, describe and use adaptive fuzzy system.
• 3. Know the concept of artificial neural networks.
• 4. Interpret mapping and recurrent networks.
• 5. Provide adequate knowledge of application of fuzzy logic control to real time systems.

UNIT - I

Fuzzy Set Theory and Fuzzy Logic Control: Basic concepts of fuzzy sets- Operations on fuzzy sets- Fuzzy relation equations- Fuzzy logic control- Fuzzification –Defuzzification- Knowledge base- Decision making logic- Membership functions – Rule base.

UNIT - II

Adaptive Fuzzy Systems: Performance index- Modification of rule base0- Modification of membership functions- Simultaneous modification of rule base and membership functions- Genetic Algorithms- Adaptive fuzzy system- Neuro fuzzy systems.

UNIT - III

Artificial Neural Networks: Introduction- History of neural networks- multilayer perceptions- Back propagation algorithm and its Variants- Different types of learning, examples.

UNIT - IV

Mapping and Recurrent Networks: Counter propagation –Self organization Map- Congnitron and Neocognitron- Hopfield Net- Kohonnen Nets- Grossberg Nets- Art-I, Art-II reinforcement learning

UNIT - V

Case Studies: Application of fuzzy logic and neural networks to Measurement- Control- Adaptive Neural Controllers – Signal Processing and Image Processing

TEXT BOOK:

• 1. Vallum B. R And Hayagriva V.R C++, Neural networks and Fuzzy logic, BPB Publications, New Delhi, 1996

REFERENCE BOOKS:

• 1. Fuzzy logic &Neural Networks/ Chennakesava R.Alavala/ New Age International, 2008.
• 2. Neural Networks for control, Millon W. T, Sutton R.S and Werbos P.J, MIT Press 1992.
• 3. Fuzzy sets Fuzzy logic, Klir, G.J anfd Yuan B.B Prentice Hall of India Pvt. Ltd., New Delhi.
• 4. Neural Networks and Fuzzy systems, Kosko. Prentice hall of India Pvt. Ltd., New Delhi 1994.
• 5. Introduction to Fuzzy control, Dirankov D. Hellendoorn H, Reinfrank M., Narosa Publications House, New Delhi 1996.
• 6. Introduction to Artificial Neural systems, Zurada J. M Jaico Publishing House, New Delhi, 1994. R22M.Tech. CAD/CAM JNTUH M. TECH. I Year II Sem. (CAD/CAM)