Analog VLSI neural chips for real-time identification and control

Jiann-Shiou Yang, Yiwen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analog CMOS neural chips with on-chip learning are explored to provide efficient and inexpensive electronics for various tasks in real-time identification and control. Hardware learning circuits can successfully obtain a set of synaptic weights for multilayer feedforward neural networks that approximately satisfy any nonlinear mapping in the order of milliseconds. The fast on-chip learning can identify nonlinear dynamical systems to avoid the modeling uncertainty and parameter variations in real-time. Model reference adaptive control with on-line identification using the neural chips are also proposed.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages2779-2782
Number of pages4
ISBN (Print)0780314212, 9780780314214
StatePublished - 1993
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: Oct 25 1993Oct 29 1993

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume3

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period10/25/9310/29/93

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