Course

Artificial Intelligence – a Brief Review – Pitfalls of Traditional AI – Need for Computational Intelligence – Importance of Tolerance of Imprecision and Uncertainty – Constituent Techniques – Overview of Artificial Neural Networks – Fuzzy Logic – Evolutionary Computation.

Neural Network: Biological and Artificial Neuron, Neural Networks, Supervised and Unsupervised Learning. Single Layer Perceptron – Multilayer Perceptron – Backpropagation Learning.

Neural Networks as Associative Memories – Hopfield Networks, Bidirectional Associative Memory. Topologically Organized Neural Networks – Competitive Learning, Kohonen Maps.

Fuzzy Logic: Fuzzy Sets – Properties – Membership Functions – Fuzzy Operations. Fuzzy Logic and Fuzzy Inference – Applications. Evolutionary Computation – Constituent Algorithms. Swarm Intelligence Algorithms – Overview of other Bio-inspired Algorithms – Hybrid Approaches (Neural Networks, Fuzzy Logic, Genetic Algorithms etc.).

1. LaureneFausett, Fundamentals of Neural Networks, 2ndedition,Pearson, 1993
2. Ross T J, “Fuzzy Logic with Engineering Applications”, McGraw Hill, 1997.
3. Eiben A E and Smith J E, “Introduction to Evolutionary Computing”, Second Edition, Springer, Natural Computing Series, 2007.
4. Kumar S, “Neural Networks – A Classroom Approach”, Tata McGraw Hill, 2004.
5. Engelbrecht, A.P, “Fundamentals of Computational Swarm Intelligence”, John Wiley & Sons, 2006.
6. Konar. A, “Computational Intelligence: Principles, Techniques and Applications”, Springer Verlag, 2005.