6EC6.1 NEURAL NETWORKS
  Units    Contents of the subjects
I
INTRODUCTION: Introduction to Neural Networks, Biological basis for NN, Human brain, Models of a Neuron, Directed Graphs, Feedback, Network architectures, Knowledge representation, Artificial intelligence & Neural Networks.
II

LEARNING PROCESSES: Introduction, Error –Correction learning, Memory – based learning, Hebbian learning, Competitive learning, Boltzmann learning, Learning with a Teacher & without a teacher, learning tasks, Memory, Adaptation.
III
SINGLE LAYER PERCEPTRONS: Introduction, Least-mean-square algorithm, Learning Curves, Learning rate Annealing Techniques, Perceptron, Perceptron Convergence Theorem.
IV
MULTI LAYER PERCEPTRONS: Introduction, Back-Propagation Algorithm, XOR Problem, Output representation and Decision rule, Feature Detection, Back- Propagation and Differentiation, Hessian Matrix, Generalization.
V
RADIAL-BASIS FUNCTION NETWORKS & SELF-ORGANISING MAPS: Introduction to Radial basis function networks, Cover’s Theorem on the Separability of Patterns, Interpolation Problem, Generalized Radial-Basis function networks, XOR Problem. Self-Organizing map, Summary of SOM Algorithm, Properties of the feature map.
Text/References:
• Artificial Neural Networks, Jacek M Zurada, Pws Pub Co
• Neural Networks: A Classroom Approach, Satish Kumar, TMH
• Artificial Neural Networks, Christina Ray, TMH
• Neural Networks For Pattern Reconization, Bishop, Oxford
• Neural Network In Soft Computing Framework, Swamy, Springer
• Fundamentals Of Neural Networks: Architectures, Algorithms And Applications.,
Fausett, Pearson
• Learning And Soft Computing: Support Vector Machines, Neural Networks, And Fuzzy
Logic Models, Vojislav Kacman, Pearson
• Fuzzy Logic And Neural Networks:, Chennakesava R, New Age