NDLI: Analog neural network development system with fast on line training capabilities

Content Provider	IEEE Xplore Digital Library
Author	Cancelo, G. Hansen, S.
Copyright Year	1994
Description	Author affiliation: Dept. de Electrotecnia, Univ. Nacional de La Plata, Argentina (Cancelo, G.; Hansen, S.)
Abstract	This paper introduces a system to train a specific neural network device. The system allows the use of standard learning algorithms (e.g. backpropagation, Madaline III, etc.) and generates the weights for a 128 analog neural network device called ETANN (Electrically Trainable Analog Neural Network). An ultra fast weight setting algorithm stores the internal coefficients into a real analog (not computer simulated) neural network. The trainer and programmer (NETMAP) system is able to cycle through steps of learning, weight setting and parallel processing runs, performing the so called "chip-in-the-loop" procedure. The NETMAP system integrates hardware and software to interface and take full advantage of ETANN parallel processing capabilities. Due to weight setting is a complex and computationally intensive process, a solution, based on an off-line mapping between weights and pulses of charge, is proposed to speed up the loading time.
Starting Page	1396
Ending Page	1400
File Size	430689
Page Count	5
File Format	PDF
ISBN	0780313283
DOI	10.1109/IECON.1994.397999
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-09-05
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Artificial neural networks Time of arrival estimation Computer networks Signal processing algorithms Analog computers Hardware Neurons Backpropagation algorithms Computational modeling
Content Type	Text
Resource Type	Article

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