Neural Networks

Unit 1

Basics of ANN: Models to Neuron; Basic learning laws. Activation and Synaptic Dynamics:

Activation dynamics models; Synaptic dynamics models; Stability and Convergence.

Unit 2

Analysis of Feed forward Neural Networks: Linear associative networks for pattern association; Single layer and Multilayer Perception network for pattern classification; Multilayer feed forward neural networks for pattern mapping.

Unit 3

Analysis of Feedback Neural Networks: Linear auto associative networks; Hopfield model for pattern storage; stochastic networks; Boltzmann machine for pattern environment storage.

Unit 4

Competitive Learning Neural Networks: Basic competitive learning laws; Analysis of pattern clustering networks; Analysis of self-organizing feature mapping networks.

Unit 5

Applications of ANN: Pattern classification problems; Optimization; Control.

REFERENCES:

1. J.A. Anderson, An Introduction to Neural Networks, MIT

2. Hagen Demuth Beale, Neural Network Design, Cengage Learning

3. Laurene V. Fausett, "Fundamentals of Neural Networks : Architectures, Algorithms and Applications", Pearson India

4. Kosko, Neural Network and Fuzzy Sets, PHI ELECTIVE-III MACHINE LEARNING (