JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To understand the biological neural network and to model equivalent neuron models.
• To understand the architecture, learning algorithm and issues of various feed forward and feedback neural networks.

Course Outcomes

By completing this course the student will be able to:

• Create different neural networks of various architectures both feed forward and feed backward.
• Perform the training of neural networks using various learning rules.
• Perform the testing of neural networks and do the perform analysis of these networks for various pattern recognition applications.

UNIT - I

Introduction: A Neural Network, Human Brain, Models of a Neuron, Neural Networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks Learning Process: Error Correction Learning, Memory Based Learning, Hebbian Learning, Competitive, Boltzmann Learning, Credit Assignment Problem, Memory, Adaption, Statistical Nature of the Learning Process

UNIT - II

Single Layer Perceptron: Adaptive Filtering Problem, Unconstrained Organization Techniques, Linear Least Square Filters, Least Mean Square Algorithm, Learning Curves, Learning Rate Annealing Techniques, Perceptron –Convergence Theorem, Relation Between Perceptron and Bayes Classifier for a Gaussian Environment Multilayer Perceptron: Back Propagation Algorithm XOR Problem, Heuristics, Output Representation and Decision Rule, Computer Experiment, Feature Detection

UNIT - III

Back Propagation: Back Propagation and Differentiation, Hessian Matrix, Generalization, Cross Validation, Network Pruning Techniques, Virtues, and Limitations of Back Propagation Learning, Accelerated Convergence, Supervised Learning

UNIT - IV

Self-Organization Maps (SOM): Two Basic Feature Mapping Models, Self-Organization Map, SOM Algorithm, Properties of Feature Map, Computer Simulations, Learning Vector Quantization, Adaptive Patter Classification

UNIT - V

Neuro Dynamics: Dynamical Systems, Stability of Equilibrium States, Attractors, Neuro Dynamical Models, Manipulation of Attractors as a Recurrent Network Paradigm Hopfield Models – Hopfield Models, Computer Experiment

TEXT BOOKS:

• 1. Neural Networks a Comprehensive Foundations, Simon Haykin, PHI edition.

REFERENCE BOOKS:

• 1. Artificial Neural Networks - B. Yegnanarayana Prentice Hall of India P Ltd 2005
• 2. Neural Networks in Computer Inteligance, Li Min Fu TMH 2003
• 3. Neural Networks -James A Freeman David M S Kapura Pearson Education 2004.
• 4. Introduction to Artificial Neural Systems Jacek M. Zurada, JAICO Publishing House Ed. 2006. B.TECH. BIOMEDICAL ENGINEERING