BM1003 Neural Networks and Its Application Syllabus

BM1003 NEURAL NETWORKS AND ITS APPLICATION 3 0 0 100

1. ADAPTIVE LINEAR COMBINER 9
Elementary neurophysiology and biological neural network-Artificial neural network, Adeline and Madeline.

2. BACK PROPOGATION AND ASSOCIATE MEMORY 9
Back propogation network, generalized delta rule, Bidirectional associate memory,
Hopefield memory architecture.

3. BOLTZMANN’S MACHINES AND COUNTER PROPOGATION NETWORK 9
Simulated Annealing, Boltzman completion network, Boltzman input output network, counter propogation network.

4. SELF-ORGANISING MAPS AND ADAPTIVE RESONANCE THEORY 9
Self organizing map,feature map classifier, adaptive resonance theory network, ART1,ART2.

5. SPATIOTEMPORAL NETWORKS AND NEOCOGNITRON 9
Architecture of spatiotemporal networks, Sequential competitive avalanche field, Neocognitron architecture and dataprocessing.

TOTAL : 45

TEXT BOOK
1. J.A. Freeman & David.M. Skapura, Neural networks, Algorithms applications and programming techniques, Addison Wesley, 1991. ISE Reprint 1999.

REFERENCES
1. David M. Skapura, “Building Neural Networks”, Addison Wesley, 1996.
2. Bose, “Neural Network Fundamentals with graphs, algorithms and applications”, Tata McGraw-Hill, 1995.
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