2K6 ME 805(B) / 2K6 CS 805(C) : NEURAL NETWORKS & FUZZY LOGIC

Module I (13 hours)
Introduction to artificial neural networks – biological neurons – Mc Culloch and Pitts modals of neuron – types of activation function – network architectures – knowledge representation – learning process – error-correction learning – supervised learning – unsupervised learning – single unit mappings and the perceptron – perceptron convergencetheorem (with out proof) – method of steepest descent – least mean square algorithms – adaline/medaline units – multilayer perceptrons – derivation of the back-propagation algorithm
Module II (13 hours)
Radial basis and recurrent neural networks – RBF network structure – covers theorem and the separability of patterns – RBF learning strategies – K-means and LMS algorithms – comparison of RBF and MLP networks – recurrent networks – Hopfield networks – energy function – spurious states – error performance – simulated annealing – the Boltzman machine – Boltzman learning rule – the mean field theory machine – MFT learning algorithm – applications of neural network – the XOR problem – traveling salesman problem – image compression using MLPs – character retrieval using Hopfield networks
Module III (13 hours)
Fuzzy logic – fuzzy sets – properties – operations on fuzzy sets – fuzzy relations – operations on fuzzy relations – the extension principle – fuzzy measures – membership functions – fuzzification and defuzzification methods – fuzzy controllers – Mamdani and Sugeno types – design parameters – choice of membership functions – fuzzification and defuzzification methods – applications
Module IV (13 hours)
Introduction to genetic algorithm and hybrid systems – genetic algorithms – natural evolution – properties – classification – GA features – coding – selection – reproduction – cross over and mutation operators basic GA and structure Introduction to Hybrid systems – concept of neuro-fuzzy and neuro-genetic system 

Reference books
Simon Haykins, “Neural Network a – Comprehensive Foundation”, Macmillan College, Proc, Con, Inc
Zurada J.M., “Introduction to Artificial Neural Systems, Jaico publishers
Driankov D., Hellendoorn H. & Reinfrank M., “An Introduction to Fuzzy Control”, Norosa Publishing House
Ross T.J., “Fuzzy Logic with Engineering Applications”, McGraw Hill
Bart Kosko. “Neural Network and Fuzzy Systems”, Prentice Hall, Inc., Englewood Cliffs
6 Goldberg D.E., “Genetic Algorithms in Search Optimisation and Machine Learning”, Addison Wesley
7 Suran Goonatilake & Sukhdev Khebbal (Eds.), “Intelligent Hybrid Systems”, John Wiley