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Soft Computing

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Overview

Topics Covered

1. Introduction to Soft Computing

  • Soft computing Constituents, Characteristics of Neuro Computing and Soft Computing, Difference between Hard Computing and Soft Computing, Concepts of Learning and Adaptation.

2. Neural Networks

  • Basics of Neural Networks: Introduction to Neural Networks, Biological Neural ...

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1. Introduction to Soft Computing

  • Soft computing Constituents, Characteristics of Neuro Computing and Soft Computing, Difference between Hard Computing and Soft Computing, Concepts of Learning and Adaptation.

2. Neural Networks

  • Basics of Neural Networks: Introduction to Neural Networks, Biological Neural Networks, McCulloch Pitt model

  • Supervised Learning algorithms: Perceptron (Single Layer, Multi layer), Linear separability, Delta learning rule, Back Propagation algorithm

  • Un-Supervised Learning algorithms: Hebbian Learning, Winner take all, Self Organizing Maps, Learning Vector Quantization.

3. Fuzzy Set Theory

  • Classical Sets and Fuzzy Sets, Classical Relations and Fuzzy Relations, Properties of membership function, Fuzzy extension principle, Fuzzy Systems- fuzzification, defuzzification and fuzzy controllers.

4. Hybrid system

  • Introduction to Hybrid Systems, Adaptive Neuro Fuzzy Inference System(ANFIS).

5. Introduction to Optimization Techniques

  • Derivative based optimization - Steepest Descent, Newton method.

  • Derivative free optimization- Introduction to Evolutionary Concepts.

6. Genetic Algorithms and its applications

  • Inheritance Operators, Cross over types, inversion and Deletion, Mutation Operator, Bit-wise Operators, Convergence of GA, Applications of GA
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