IC 1403 NEURAL NETWORK AND FUZZY LOGIC CONTROL 3 0 0 100

AIM

To cater the knowledge of Neural Networks and Fuzzy Logic Control and use

these for controlling real time systems.

OBJECTIVES

i. To expose the students to the concepts of feed forward neural networks.

ii. To provide adequate knowledge about feed back neural networks.

iii. To teach about the concept of fuzziness involved in various systems. To provide adequate knowledge about fuzzy set theory.

iv. To provide comprehensive knowledge of fuzzy logic control and

adaptive fuzzy logic and to design the fuzzy control using genetic algorithm.

v. To provide adequate knowledge of application of fuzzy logic control to real time systems.

1. ARCHITECTURES 9

Introduction – Biological neuron – Artificial neuron – Neuron modeling – Learning rules – Single layer – Multi layer feed forward network – Back propagation – Learning factors.

2. NEURAL NETWORKS FOR CONTROL 9

Feed back networks – Discrete time hop field networks – Transient response of continuous time networks – Applications of artificial neural network - Process identification – Neuro controller for inverted pendulum.

3. FUZZY SYSTEMS 9

Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification – Defuzzification – Fuzzy rules.

4. FUZZY LOGIC CONTROL 9

Membership function – Knowledge base – Decision-making logic – Optimisation of membership function using neural networks – Adaptive fuzzy system – Introduction to genetic algorithm.

5. APPLICATION OF FLC 9

Fuzzy logic control – Inverted pendulum – Image processing – Home heating system – Blood pressure during anesthesia – Introduction to neuro fuzzy controller.

L = 45 Total = 45

TEXT BOOKS

1. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’, Jaico Publishing home, 2002.

2. Timothy J. Ross, ‘Fuzzy Logic with Engineering Applications’, Tata McGraw Hill, 1997.

REFERENCE BOOKS

1. Laurance Fausett, Englewood cliffs, N.J., ‘Fundamentals of Neural Networks’, Pearson Education, 1992.

2. H.J. Zimmermann, ‘Fuzzy Set Theory & its Applications’, Allied Publication Ltd., 1996.

3. Simon Haykin, ‘Neural Networks’, Pearson Education, 2003.

4. John Yen & Reza Langari, ‘Fuzzy Logic – Intelligence Control & Information’, Pearson

Education, New Delhi, 2003.

AIM

To cater the knowledge of Neural Networks and Fuzzy Logic Control and use

these for controlling real time systems.

OBJECTIVES

i. To expose the students to the concepts of feed forward neural networks.

ii. To provide adequate knowledge about feed back neural networks.

iii. To teach about the concept of fuzziness involved in various systems. To provide adequate knowledge about fuzzy set theory.

iv. To provide comprehensive knowledge of fuzzy logic control and

adaptive fuzzy logic and to design the fuzzy control using genetic algorithm.

v. To provide adequate knowledge of application of fuzzy logic control to real time systems.

1. ARCHITECTURES 9

Introduction – Biological neuron – Artificial neuron – Neuron modeling – Learning rules – Single layer – Multi layer feed forward network – Back propagation – Learning factors.

2. NEURAL NETWORKS FOR CONTROL 9

Feed back networks – Discrete time hop field networks – Transient response of continuous time networks – Applications of artificial neural network - Process identification – Neuro controller for inverted pendulum.

3. FUZZY SYSTEMS 9

Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification – Defuzzification – Fuzzy rules.

4. FUZZY LOGIC CONTROL 9

Membership function – Knowledge base – Decision-making logic – Optimisation of membership function using neural networks – Adaptive fuzzy system – Introduction to genetic algorithm.

5. APPLICATION OF FLC 9

Fuzzy logic control – Inverted pendulum – Image processing – Home heating system – Blood pressure during anesthesia – Introduction to neuro fuzzy controller.

L = 45 Total = 45

TEXT BOOKS

1. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’, Jaico Publishing home, 2002.

2. Timothy J. Ross, ‘Fuzzy Logic with Engineering Applications’, Tata McGraw Hill, 1997.

REFERENCE BOOKS

1. Laurance Fausett, Englewood cliffs, N.J., ‘Fundamentals of Neural Networks’, Pearson Education, 1992.

2. H.J. Zimmermann, ‘Fuzzy Set Theory & its Applications’, Allied Publication Ltd., 1996.

3. Simon Haykin, ‘Neural Networks’, Pearson Education, 2003.

4. John Yen & Reza Langari, ‘Fuzzy Logic – Intelligence Control & Information’, Pearson

Education, New Delhi, 2003.

EmoticonEmoticon