GBTU / MTU Syllabus of INTRODUCTION TO SOFT COMPUTING [Effective from the session : 2012-13]

EOE-031/EOE-041: INTRODUCTION TO SOFT COMPUTING

(Neural Networks, Fuzzy Logic and Genetic Algorithm)

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Unit-I

Neural Networks-1(Introduction & Architecture)

Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions,

Neural network architecture: single layer and multilayer feed forward networks, recurrent

networks.Various learning techniques; perception and convergence rule,

Auto-associative and hetro-associative memory.

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Unit-II

Neural Networks-II (Back propogation networks)

Architecture: perceptron model, solution, single layer artificial neural network, multilayer

perception model; back propogation learning methods, effect of learning rule co-efficient ;back

propagation algorithm, factors affecting backpropagation training, applications.

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Unit-III

Fuzzy Logic-I (Introduction)

Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set theory and operations,

Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp conversion.

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Unit-IV

Fuzzy Logic –II (Fuzzy Membership, Rules)

Membership functions, interference in fuzzy logic, fuzzy if-then rules, Fuzzy implications and

Fuzzy algorithms, Fuzzyfications & Defuzzificataions, Fuzzy Controller, Industrial applications.

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Unit-V

Genetic Algorithm(GA)

Basic concepts, working principle, procedures of GA, flow chart of GA, Genetic representations,

(encoding) Initialization and selection, Genetic operators, Mutation, Generational Cycle,

applications.

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Text Books:

1. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic

Algorithm:Synthesis and Applications” Prentice Hall of India.

2. N.P.Padhy,”Artificial Intelligence and Intelligent Systems” Oxford University Press.

Reference Books:

3. Siman Haykin,”Neural Netowrks”Prentice Hall of India

4. Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India.

5. Kumar Satish, “Neural Networks” Tata Mc Graw Hill