UNIT-I
Introduction, Soft Computing concept
explanation, brief description of separate theories.
Neural Networks and Probabilistic
Reasoning; Biological and artificial neuron, neural networks and
their classification. Adaline, Perceptron, Madaline and BP (Back
Propagation) neural networks. Adaptive feedforward multilayer networks.
Algorithms: Marchand, Upstart, Cascade correlation, Tilling. RBF and RCE
neural networks. Topologic organized neural network, competitive learning,
Kohonen maps.
UNIT-II
CPN , LVQ, ART, SDM and Neocognitron
neural networks. Neural networks as associative memories (Hopfield, BAM).
Solving optimization problems using neural networks. Stochastic neural
networks,Boltzmann machine.
UNIT-III
Fundamentals of fuzzy sets and fuzzy
logic theory, fuzzy inference principle. Examples of use of fuzzy logic in
control of real-world systems.
UNIT-IV
Fundamentals of genetic programming,
examples of its using in practice. Genetic Algorithms Applications of GA's
– Class.
UNIT-V
Fundamentals of rough sets and chaos
theory. Hybrid approaches (neural networks, fuzzy logic,
genetic algorithms, rough sets).
BOOKS:
1. Cordón, O., Herrera, F., Hoffman,
F., Magdalena, L.: Genetic Fuzzy systems, World Scientific Publishing Co.
Pte. Ltd., 2001, ISBN 981-02-4016-3
2. Kecman, V.: Learning and Soft
Computing, The MIT Press, 2001, ISBN 0-262-11255-8
3. Mehrotra, K., Mohan, C., K.,
Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997,ISBN
0-262-13328-8
4. Munakata, T.: Fundamentals of the
New Artificial Intelligence, Springer-Verlag New York, Inc., 1998.ISBN
0-387-98302-3
5. Goldberg : Introduction to
Genetic Algorithms
6. Jang, “ Nero-Fuzzy & Soft
Computing”, Pearsons
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