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Adaptive Analog VLSI Neural Systems de M. Jabri

Adaptive Analog VLSI Neural Systems

Autor M. Jabri, R.J. Coggins, B. G. Flower
en Limba Engleză Paperback – 31 dec 1995
Adaptive Analog VLSI Neural Systems is the first practical book on neural networks learning chips and systems. It covers the entire process of implementing neural networks in VLSI chips, beginning with the crucial issues of learning algorithms in an analog framework and limited precision effects, and giving actual case studies of working systems.
The approach is systems and applications oriented throughout, demonstrating the attractiveness of such an approach for applications such as adaptive pattern recognition and optical character recognition.
Dr Jabri and his co-authors from AT&T Bell Laboratories, Bellcore and the University of Sydney provide a comprehensive introduction  to VLSI neural networks suitable for research and development staff and advanced students.
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Specificații

ISBN-13: 9780412616303
ISBN-10: 0412616300
Pagini: 262
Ilustrații: VII, 262 p. 83 illus.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:1996
Editura: SPRINGER NETHERLANDS
Colecția Springer
Locul publicării:Dordrecht, Netherlands

Public țintă

Research

Cuprins

Overview. Introduction to neural computing. MOS devices and circuits. Analogue VLSI building blocks. Kakadu - a micropower neural network. Supervised learning in an analog framework. A micropower intracardiac electrogram classifier. On-chip perturbation based learning. An analog memory technique. Switched capacitor techniques. A high speed image understanding system. A Boltzmann machine learning system. References. Index.

Notă biografică

Marwan Jabri is a Reader, Richard Coggins is a Research Engineer and Barry Flower is a Research Fellow at SEDAL, Sydney University Electrical Engineering, Australia.

Textul de pe ultima copertă

Adaptive Analog VLSI Neural Systems is the first practical book on neural networks learning chips and systems. It covers the entire process of implementing neural networks in VLSI chips, beginning with the crucial issues of learning algorithms in an analog framework and limited precision effects, and giving actual case studies of working systems.
The approach is systems and applications oriented throughout, demonstrating the attractiveness of such an approach for applications such as adaptive pattern recognition and optical character recognition.
Dr Jabri and his co-authors from AT&T Bell Laboratories, Bellcore and the University of Sydney provide a comprehensive introduction  to VLSI neural networks suitable for research and development staff and advanced students.