504184 ELECTIVE I
MACHINE INTELLIGENCE
Teaching Scheme Examination Scheme
Lectures: 3 Hrs./Week Theory: 100 Marks
Credit: 3
Introduction, Soft Computing intelligence, comparison with conventional Artifical
Intelligence, soft computing characteristics, Fuzzy sets, Fuzzy rules and Fuzzy inference
systems, Different fuzzy Models : Mamdani, Sugeno, Tsu Kamoto, Fuzzy modeling, Least
squares methods for system identification, Derivative based optimization.
Neural networks, Adaptive networks, Supervised learning Neural networks, Perceptron,
Backpropagation Multilayer perceptron , Radial basis function networks, Learning from
reinforcement, Dynamic programming, Competitive learning, Kohonen’s self organizing
networks, Principle component networks, LVQ, Hopfield networks.
Adaptive Neuro-Fuzzy interface systems, Advanced Neuro-Fuzzy modeling,
Data clustering algorithms, Neuro-Fuzzy control, Fuzzy filtered neural network, Genetic
algorithms in game playing.
References:
1. S. R. Jang, C.T. Sun, E. Mizutani,‘ Neuro-Fuzzy and Soft Computing’, Pearson
Education, ISBN 81-297-0324-6.
2. B. Kosko, ‘Neural Networks and Fuzzy Systems : a dynamical systems approach’
Prentice Hall Publication.
3. Simon Haykin, ‘ Neural Networks : comprehensive foundation’, Prentice Hall, ISBN-10:
0132733501.
4. Jacek M. Zurada , ‘Introduction to Artifical Neural Systems’, Jaico publications
Reference http://www.unipune.ernet.in/stud_info/Syllabi/Syllabus_2008.html
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