Neural Networks characteristics, History of development, 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 algorithms, Kohonen’s
feature maps.
Radial Basis Function neural networks , basic learning laws in RDF nets, Recurrent networks,
recurrent back propagation, Real time Recurrent learning algorithms.
Introduction to Counter Propagation Networks, CMAC networks, ART networks.
Application of neural nets such as pattern recognition, optimization, associative memories,
vector quantization, control.
Application in speech and decision making.