Unit-I

Soft Computing: Introduction of soft computing, soft computing vs. hard computing, various types of soft computing techniques, applications of soft computing.

Unit-II

Artificial Intelligence: Introduction, Various types of production systems, characteristics of production systems, breadth first search, depth first search techniques, other Search Techniques like hill Climbing, Best first Search, A* algorithm, AO* Algorithms and various types of control strategies.

Unit-III

Fuzzy Logic: Crisp set and Fuzzy set, basic concepts of fuzzy sets, membership functions. Basic operations on fuzzy sets, Properties of fuzzy sets, Fuzzy relations. Propositional logic and Predicate logic, fuzzy If – Then rules, fuzzy mapping rules and fuzzy implication functions, Applications.

Unit-IV

Neural Networks: Basic concepts of neural networks, Neural network architectures, Learning methods, Architecture of a back propagation network, Applications.

Unit-V

Genetic Algorithms: Basic concepts of genetic algorithms, encoding, genetic modeling.

Hybrid Systems: Integration of neural networks, fuzzy logic and genetic algorithms.

Course Outcomes
CO-1: Understand the various types of soft computing techniques
CO-2: Understand the concepts of artificial intelligence.
CO-3: Understand and apply the concepts fuzzy logic in optimization problems.
CO-4: Understand the concepts of neural networks.
CO-5: Understand the concepts of genetic algorithms.

1. S. Rajasekaran and G.A.Vijaylakshmi Pai.. Neural Networks Fuzzy Logic, and Genetic Algorithms, Prentice Hall of India.
2. K.H.Lee.. First Course on Fuzzy Theory and Applications, Springer-Verlag.
3. J. Yen and R. Langari.. Fuzzy Logic, Intelligence, Control and Information, Pearson Education.