Unit II: Supervised Learning: Perceptron learning,- Single layer/multilayer, linear Separability, Adaline, Madaline, Back propagation network, RBFN. Application of Neural network in forecasting, data compression and image compression.
Unit IV: Fuzzy Set: Basic Definition and Terminology, Set-theoretic Operations, Member Function,
Formulation and Parameterization, Fuzzy rules and fuzzy Reasoning, Extension Principal and Fuzzy Relations, Fuzzy if-then Rules, Fuzzy Inference Systems. Hybrid system including neuro fuzzy hybrid, neuro genetic hybrid and fuzzy genetic hybrid, fuzzy logic controlled GA. Application of Fuzzy logic in solving engineering problems.
IT 830 Soft Computing References:-
- S.N. Shivnandam, “Principle of soft computing”, Wiley.
- S. Rajshekaran and G.A.V. Pai, “Neural Network , Fuzzy logic And Genetic Algorithm”, PHI.
- Jack M. Zurada, “Introduction to Artificial Neural Network System” JAico Publication.
- Simon Haykins, “Neural Network- A Comprehensive Foudation”
- Timothy J.Ross, “Fuzzy logic with Engineering Applications”, McGraw-Hills 1.
- Form a perceptron net for basic logic gates with binary input and output.
- Using Adaline net, generate XOR function with bipolar inputs and targets.
- Calculation of new weights for a Back propagation network, given the values of input pattern, output pattern, target output, learning rate and activation function.
- Construction of Radial Basis Function Network.
- Use of Hebb rule to store vector in auto associative neural net.
- Use of ART algorithm to cluster vectors.
- Design fuzzy inference system for a given problem.
- Maximize the function y =3x**2+ 2 for some given values of x using Genetic algorithm.
- Implement Travelling salesman problem using Genetic Algorithm.
- Optimisation of problem like Job shop scheduling using Genetic algorithm