NN (B-TECH Computer Science Engineering 7-8th)

Section A

Overview of biological neurons: Structure of biological neurons relevant to ANNs.

Fundamental concepts of Artificial Neural Networks: Models of ANNs; Feedforward & feedback

networks; learning rules; Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff

learning rule, correction learning rule, Winner –lake all elarning rule, etc.

Section B

Single layer Perception Classifier: Classification model, Features & Decision regions; training &

classification using discrete perceptron, algorithm, single layer continuous perceptron networks for

linearlyseperable classifications.

Multi-layer Feed forward Networks: linearly non-seperable pattern classification, Delta learning rule for

multi-perceptron layer, Generalized delta learning rule, Error back-propagation training, learning factors,

Examples.

Section C

Single layer feed back Networks: Basic Concepts, Hopfield networks, Training & Examples.

Associative memories: Linear Association, Basic Concepts of recurrent Auto associative memory:

rentrieval algorithm, storage algorithm; By directional associative memory, Architecture,

Association encoding & decoding, Stability.

Section D

Self organizing networks: UN supervised learning of clusters, winner-take-all learning, recall mode,

Initialisation of weights, seperability limitations