EE456

ARTIFICIAL NEURAL NETWORKS

Objectives

  • To gain exposure in the field of neural networks and relate the human neural system into the digital world
  • To provide knowledge of computation and dynamical systems using neural networks

 

Outcomes

  • Acquire skill set to innovate and build a smart and intelligent engineering application using ANN

 

Unit – I

Perceptron Architecture- Single-Neuron Perceptron- Multi-Neuron Perceptron-

 

Unit – II

Perceptron Learning Rule- Constructing Learning Rules- Training Multiple-Neuron Perceptrons.

 

Unit – III

Simple Associative Networks- Unsupervised Hebb Rule- Hebb Rule with Decay-Instar Rule-Outstar Rule- Kohonen Rule.

 

Unit – IV

Adaline Network- Madaline Network -Mean Square Error- LMS Algorithm- Back Propagationa Neural networks – Hopfield Networks

 

Unit – V

Adaptive Filtering- Adaptive Noise Cancellation- Forecasting – Neural control applications – Character recognition.

 

TEXT BOOKS

  • Hagan Demuth Beale, ‘Neural network design’, PWS publishing company, 1995
  • Freeman, J.A and Skapura, D.M., ‘Neural networks-Algorithms, applications and programming techniques’ Addison Wesley, 1991
  • Satish Kumar, Neural Networks – A classroom approach’, Tata McGraw-Hill Publishing Company Limited, 2004