CL7204 SOFT COMPUTING TECHNIQUES

CL7204   SOFT COMPUTING TECHNIQUES        

UNIT I   INTRODUCTION AND ARTIFICIAL NEURAL NETWORKS

Introduction of soft computing - soft computing vs. hard computing- various types of soft computing techniques- applications of soft computing-Neuron- Nerve structure and synapse- Artificial Neuron and its model- activation functions- Neural network architecture- single layer and multilayer feed forward networks- McCullochPitts neuron model- perceptron model- Adaline and Madaline- multilayer perception model- back propogation learning methods- effect of learning rule coefficient -back propagation algorithm- factors affecting back propagation training- applications. 

UNIT II  ARTIFICIAL NEURAL NETWORKS

Counter propagation network- architecture- functioning & characteristics of counter- Propagation network-Hopfield/ Recurrent network- configuration- stability constraints-associative memory- and characteristics- limitations and applications- Hopfield v/s Boltzman machine- Adaptive Resonance Theory- Architecture- classifications-Implementation and training-Associative Memory. 

UNIT III  FUZZY LOGIC SYSTEM

Introduction to crisp sets and fuzzy sets- basic fuzzy set operation and approximate reasoning. Introduction to fuzzy logic modeling and control- Fuzzification- inferencingand defuzzificationFuzzy knowledge and  rule bases-Fuzzy modeling and control schemes for nonlinear systems. Self organizing  fuzzy logic control- Fuzzy logic control for nonlinear time delay system. 

UNIT IV  GENETIC ALGORITHM

Basic concept of Genetic algorithm and detail algorithmic steps-adjustment of free Parameters- Solution of typical control problems using genetic  algorithm- Concept on some other search techniques like tabu search and ant colony search techniques for solving optimization problems.  

UNIT V         APPLICATIONS

GA application to power system optimization problem- Case studies: Identification and control of linear and nonlinear dynamic systems using Matlab-Neural Network toolbox.  Stability analysis of Neural Network  interconnection systems- Implementation of fuzzy logic controller using  Matlab fuzzy logic toolbox-Stability analysis of fuzzy control systems. 

  

REFERENCES 

1.. Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms And        Applications,  Pearson Education,

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

3.  Zimmermann H.J. "Fuzzy set theoryand its Applications" Springer international  edition,       2011. 

4.  David E.Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”,       Pearson Education, 2009. 

5.  W.T.Miller, R.S.Sutton and P.J.Webrose, “Neural Networks for Control”, MIT Press, 1996. 

Fuzzy Logic With Engineering Applications (English) 3rd Edition