Electronics and Telecommunication Engineering - Vishwakarma ...

Bansilal Ramnath Agarwal Charitable Trust’s
Vishwakarma Institute of Technology, Pune – 411 037
Department of Electronics and Telecommunication Engineering
FF No.: 654
EC42110:: ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC
Credits: 03
Teaching Scheme: - Theory 3 Hrs/Week
Prerequisites: Nil
Objectives:
• To give overview of Neural network systems and standards across the globe.
• To create awareness in the current trends and technologies in Neural networks
• To learn different neural Networks.
• Mapping with PEOs:2,3,4,6,7,8,9
Unit 1 Introduction to ANN :-
(4Hrs)
• History of Neural networks, Neural net architecture, Neural learning, Evaluation of
networks, Implementation.
• Application of neural networks.
Unit 2 Supervised Learning
(10Hrs)
• Perceptions , Linear separability, preceptron training algorithms, modifications,
Support vector machines, Multilevel discrimination, back propagation algorithm.
Adaptive multilayer networks, predication networks, Polynomial Networks.
• Radial basis functions, probabilistic networks.
UNIT 3 Unsupervised Learning
(8Hrs)
• Winner-Takes –All network, Learning vector quantization, counter propagation
networks, Adaptive Resonance theory, Topological Organized networks, Distance
based learning, Max Net, Competitive Net.
• Principal Component Analysis.
UNIT 4 Associative Learning
(10Hrs)
• Associative non iterative procedures for association, Hop field networks,
Optimization, Learning using Hopfield networks, Brain state in a box network.
• Boltzman machines, Hetero-associators.
UNIT 5 Evolutionary Optimization
(8Hrs)
• Optimization and search, Evolutionary Computation, Evolutionary Algorithms for
training neural networks, Learning connection weights, Learning architectures.
• Hybrid evolutionary Approaches.
Structure & Syllabus of B.E (E&TC) Program – Pattern ‘C11’, Rev01, dt. 2/4/2011
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