CSE-407- E Neural Networks
CSE-407- E Neural Networks
Class Work: 50
Exam: 100
Unit-1: Overview of biological neurons: Structure of biological neurons relevant to ANNs.
Unit-2: Fundamental concepts of Artificial Neural Networks: Models of ANNs; Feedforward & feedback networks; learning rules; Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff learning rule, correction learning rule, Winner –lake all elarning rule, etc.
Unit-3: Single layer Perception Classifier: Classification model, Features & Decision regions; training & classification using discrete perceptron, algorithm, single layer continuous perceptron networks for linearlyseperable classifications.
Unit-4: Multi-layer Feed forward Networks: linearly non-seperable pattern classification, Delta learning rule for multi-perceptron layer, Generalized delta learning rule, Error back-propagation training, learning factors, Examples.
Unit-5: Single layer feed back Networks: Basic Concepts, Hopfield networks, Training & Examples.
Unit-6: Associative memories: Linear Association, Basic Concepts of recurrent Auto associative memory: rentrieval algorithm, storage algorithm; By directional associative memory, Architecture, Association encoding & decoding, Stability.
Unit-7: Self organizing networks: UN supervised learning of clusters, winner-take-all learning, recall mode, Initialisation of weights, seperability limitations
Text Books:
- Introduction to artificial Neural systems by Jacek M. Zurada, 1994, Jaico Publ. House.
Reference Books:
- “Neural Networks :A Comprehensive formulation”, Simon Haykin, 1998, AW
- “Neural Networks”, Kosko, 1992, PHI.
- “Neural Network Fundamentals” – N.K. Bose , P. Liang, 2002, T.M.H
Note: Eight questions will be set in all by the examiners taking at least one question from each unit. Students will be required to attempt five questions in all.
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