CSE - Centurion University

PECS4203PRINCIPLE OF ARTIFICIAL NEURAL
NETWORKS (3-1-0)
MODULE 1 (16 Hrs.)
Introduction to Artificial Neural Networks
Introduction, General characteristics of the human brain, Benefits of the ANNs, Applications of the
artificial neural networks, Computational model of the neuron, Structure of a neural net (topology),
Multilayer feed forward neural networks (MLFFNNs), Pattern classification and regression using
MLFFNNs, Bayesian neural networks.
MODULE 2 (16 Hrs)
Learning Methods: Supervised learning, unsupervised learning
Radial basis function networks: RBF networks for pattern classification, RBF networks for function
approximation.
Linear Models for Regression and Classification
Polynomial curve fitting, Bayesian curve fitting, linear basis function models, Bayesian linear
regression, Least squares for classification, Logistic regression for classification.
MODULE 3 (18 Hrs)
Perceptron
Introduction, Convergence Theorem of the Perceptron, Virtues and limitations, Adaline and Madaline
Multilayer Perceptron
Introduction, Algorithm of Back propagation, Learning rate and momentum, Algorithms of Second
order, Pruning
Self- Organizing Map (SOM)
Introduction, Topology, Learning rule, Operation stage of SOM network, Geometrical interpretation
Text Books:
1. B.Yegnanarayana, Artificial Neural Networks, Prentice Hall of India, 1999
2. Satish Kumar, Neural Networks – A Classroom Approach, Tata McGraw-Hill, 2003
3. S.Haykin, Neural Networks – A Comprehensive Foundation, Prentice Hall, 1998
4. C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006
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