CSVTU BE VIII Semester IT Neural Network and Fuzzy Logic Syllabus


Semester:VIII Branch: Information Technology

Subject: Neural Network and Fuzzy Logic

UNIT-1 Introduction to Artificial Neural Networks:

Elementary Neurophysiology, Models of a Neuron, Neural Networks viewed as directed graphs,

Feedback, from neurons to ANN, Artificial Intelligence and Neural Networks; Network

Architectures, Single-layered Feed forward Networks, Multi-layered Feedforward Networks,

Recurrent Networks, Topologies.

UNIT-2 Learning and Training:

Activation and Synaptic Dynamics, Hebbian, Memory based, Competitive, Error-Correction

Learning, Credit Assignment Problem: Supervised and Unsupervised learning, Memory models,

Stability and Convergence, Recall and Adaptation.

UNIT-3 A Survey of Neural Network Models:

Single-layered Perceptron – least mean square algorithm, Multi-layered Perceptrons – Back

propagation Algorithm, XOR – Problem, The generalized Delta rule, BPN Applications, Adalines

and Madalines – Algorithm and applications.

UNIT-4 Applications:

Talking Network and Phonetic typewriter: Speech Generation and Speech recognition,

Neocognitron – Character Recognition and Handwritten Digit recognition, Pattern Recognition


UNIT-5 Neural Fuzzy Systems:

Introduction to Fuzzy sets, operations, relations, Examples of Fuzzy logic, Defuzzyfication,

Fuzzy Associative memories, Fuzziness in neural networks and examples,

Text Books:

1. Artificial Neural Networks by B. Yagna Narayan, PHI

2. Neural Networks Fuzzy Logic & Genetic Alogrithms by Rajshekaran & Pai, Prentice Hall

Reference Books:

1. Neural Networks by James A. Freeman and David M. Strapetuns, Prentice Hall,.

3. Neural Network & Fuzzy System by Bart Kosko, PHI.

4. Neural Network Design by Hagan Demuth Deale Vikas Publication House

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