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Syllabus ANN

This document outlines the syllabus for an Artificial Neural Networks course. The course objectives are to study biological and artificial neural networks, applications of ANN, and pattern recognition tasks using ANN. The course contains 3 units that cover: 1) introduction to ANN and backpropagation networks, 2) activation and synaptic dynamics models and basic ANN units for pattern recognition, and 3) competitive learning neural networks and applications of ANN like pattern classification and speech recognition. The course is 3 credits and involves 45 total hours.

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NIRMAL KUMAR
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230 views2 pages

Syllabus ANN

This document outlines the syllabus for an Artificial Neural Networks course. The course objectives are to study biological and artificial neural networks, applications of ANN, and pattern recognition tasks using ANN. The course contains 3 units that cover: 1) introduction to ANN and backpropagation networks, 2) activation and synaptic dynamics models and basic ANN units for pattern recognition, and 3) competitive learning neural networks and applications of ANN like pattern classification and speech recognition. The course is 3 credits and involves 45 total hours.

Uploaded by

NIRMAL KUMAR
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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1.

Syllabus of the subject

CHANDIGARH UNIVERSITY, GHARUAN


Artificial Neural Networks (CSA-403)
L T P
3 - -
Credits 3.0

Total
Hours: 45

Course Objectives:

 To study basics of biological Neural Network.


 To study basics of artificial Neural Network
 To study applications of ANN
 To study different pattern recognition task using ANN.

Unit-I

Introduction to ANN: Features, structure and working of Biological Neural Network, Trends in
Computing Comparison of BNN and ANN

Basics of Artificial Neural Networks: History of neural network research, characteristics of neural
networks terminology, models of neuron Mc Culloch – Pitts model, Perceptron, Adaline model, Basic
learning laws, Topology of neural network architecture

Backpropagation networks (BPN): Architecture of feed forward network, single layer ANN, multilayer
perceptron, back propagation learning, input - hidden and output layer computation, backpropagation
algorithm, applications, selection of tuning parameters in BPN, Numbers of hidden nodes, learning.

Unit-II

Activation & Synaptic Dynamics: Introduction, Activation Dynamics models, synaptic Dynamics
models, stability and convergence, recall in neural networks.

Basic functional units of ANN for pattern recognition tasks: Basic feedforward, Basic feed back and
basic competitive learning neural network, Pattern association, pattern classification and pattern mapping
tasks.

a) Feedforward neural networks –

- Linear responsibility X-OR problem and solution.


- Analysis of pattern mapping networks summary of basic gradient search methods.

b) Feed back neural networks Pattern storage networks, stochastic networks and simulated annealing,
Boltzmann machine and Boltzmann learning

Unit-III

Competitive learning neural networks: Components of CL network pattern clustering and feature
mapping network, ART networks, Features of ART models, character recognition using ART network.

Applications of ANN: Pattern classification – Recognition of Olympic games symbols, Recognition of


printed Characters. Neocognitron – Recognition of handwritten characters.

NET Talk: to convert English text to speech. Recognition of consonant vowel (CV) segments, texture
classification and segmentation

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