Course

Artificial Intelligence (AI): A Brief review – Pitfalls of Traditional AI–Why computational intelligence (CI)– Concepts of CI – Importance of tolerance of imprecision and uncertainty– Constituent techniques of CI– overview of Artificial Neural Networks, Fuzzy Logic, Evolutionary Computation.

Fuzzy Logic:Introduction – the case of imprecision, the utility and limitation of fuzzy systems. Classical sets and Fuzzy sets: operations, properties and mapping.

Classical relations and fuzzy relations: cardinality, operations, properties and composition – tolerance and equivalence relations. Properties of membership function, fuzzification and defuzzification. Logic and fuzzysystems. Fuzzy control systems – Aircraft landing control problems.

Evolutionary computation: Introduction – Constituent algorithms – Using Genetic Algorithm for solving simple optimization problems. Swarm intelligence algorithms – Overview of other bio-inspired algorithms – Over view of Hybrid approaches (neural networks, fuzzy logics, genetic algorithm etc).

Text Books:

1. Kumar ‘Neural Networks – A classroom approach’, TMH, 2014.
2. Ross T J ‘Fuzzy Logic with Engineering Applications’, TMH,2002
3. Eiben A E and Smith J E, ‘ Introduction to Evolutionary Computing’, Second Edition, Springer, Natural Computing Series, 2017.

Reference Books:

1. Konar A, ‘Computational Intelligence : Principles, Techniques and Applications”, Springer Verlat, 2005.
2. Engelbercht AP, ‘Fundamentals of Computational Swarm Intelligence’, John Wiley and Sons, 2005.

3. Jang J S R and Sun C T ,Mizutani E, ‘Neuron – Fuzzy and Soft Computing’ , PHI,2002
4. Rajasjekaran S and VijayalakshmiPai G A ‘Neural Networks, Fuzzy Logic and Genetic Algorithm’, PHI, 2003.