WITH EFFECT FROM THE ACADEMIC YEAR 2013–2014

CS 415

SOFT COMPUTING

                                                                              (Elective-III)

Instruction                                                                                                                                                                                        4  Periods per week

Duration of University Examination                                                                                                                                                   3  Hours

University Examination                                                                                                                                                                     75 Marks

Sessional                                                                                                                                                                                         25 Marks

UNIT-I

Introduction: Neural networks, application scope of neural networks, fuzzy logic, genetic algorithm, hybrid systems, Soft computing. Artificial neural networks: Fundamental concepts, Evolution of neural networks, basic model of Artificial neural networks, Important terminology of ANNs, McCulloch-pitts neuron model, Linear separability, Hebb Network Supervised Learning Network: Perceptron networks, adaptive linear neuron (Adaline), Multiple adaptive linear neuron, Back propagation network, Radial basis Function network (Architecture& Training algorithms)

UNIT-II

Associative Memory Networks: Training algorithm for pattern Association, Associative memory network, Hetroassociative memory network (Architecture& Training algorithm), Bidirectional associative memory network Architecture, Discrete Bidirectional associative memory network, Continuous BAM ,Analysis of hamming distance, Energy function and storage capacity, Hopfield networks discrete &continuous. Unsupervised Learning Networks: Fixed weight competitive Nets, Kohonen self organizing network, Learning vector quantization (Architecture& Training algorithm) Adaptive Resonance theory network. Special networks: Simulated Annealing Networks,Boltzmann machine, Gaussian machine

UNIT-III

Fuzzy Logic: Introduction to Classical sets and fuzzy sets, Classical sets,Fuzzy sets: Operations and Properties. Fuzzy Relations: Cardinality, Operations and Properties, Equivalence & tolerance. Membership function: Fuzzification, membership value assignment: Inference, rank ordering, angular fuzzy sets

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