Neural Networks characteristics: History of development in neural Networks Principles,
Artificial Neural Net terminology, Model of a neuron, topology, learning types of learning
supervised unsupervised, re-inforcement learning.
Basic Hopfield Model: the perceptron, linear separability, Basic learning laws : Hebb's rule,
Delta rule, Widrow & Hoff LMS learning rule, correlation learning rule, instar and outstar
learning rules.
Unsupervised learning: competitive learning, K-means clustering algorethm, Kohonen's
feature maps.
Radial Basis: Function neural networks, basic learning Laws in RBF nets, Recurrent
networks, recurrent back propagation, Real Time Recurrent learning algorithm. Introduction
to counter Propagation networks, CMAC networks, ART networks.
Applications of neural nets such as pattern recoginition: optimization, associative
memories, vector quantization, control,Applications in speech and decision making.
Fuzzy Logic : Basic concepts of Fuzzy Logic, Fuzzy vs Crisp set, Linguistic variables,
membership functions, operations of fuzzy sets, fuzzy IF-THEN rules, variable inference,
techniques, defuzzication techniques, basic fuzzy inference algorithm, Applications of fuzzy
logic, Fuzzy system design, Implementation of fuzzy system, Useful tools supporting design.