RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA
(University of Technology of Madhya Pradesh)
COURSE OF STUDY AND SCHEME OF EXAMINATION
MASTER OF COMPUTER APPLICATIONS (MCA)
W.E.F. 2005-2006
MCA-504 Elective-II (E-II)C Soft Computing Syllabus
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
Note : Paper is to be set unit wise with internal choice.
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