Course Syllabus

Computational intelligence (CI): Adaptation, Self-organization and Evolution, Biological and artificial neuron, Neural Networks Basic Concepts,- Single Layer perceptron-Multilayer perceptron- Supervised and unsupervised learning- Back propagation networks-Kohnen’s selforganizing networks-Hopfield networks- Implementations.

Fuzzy systems: Basic Concepts, Fuzzy sets- properties- membership functions- fuzzy operations, Applications, Implementation, Hybrid systems.

Evolutionary computing: -Introduction to Genetic Algorithms. The GA computation processnatural evolution-parent selection-crossover-mutation-properties – classification – Advances in the theory GA. Genetic Programming, Particle Swarm optimization, Ant Colony optimization, artificial immune Systems.

CI application: case studies may include image processing, digital systems, control, forecasting and time-series predictions.

1. R.C. Eberhart, “Computational Intelligence: Concept to Implementations”, Morgan Kaufmann Publishers, 2007.
2. Laurence Fausett, “Fundamentals of Neural Networks”,Prentice Hall,1994
3. Timothy J Rose, “Fuzzy Logic with Engineering Applications”, Third Edition, Wiley, 1995.
4. A Konar, “Computational Intelligence: Principles, Techniques and Applications”, Springer -Verlag, 2005.