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30 years of adaptive neural networks: perceptron, Madaline, and backpropagation

Widrow, B., Lehr, M.A.·1990·Proceedings of the IEEE

Abstract

Fundamental developments in feedforward artificial neural networks from the past thirty years are reviewed. The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described. The concept underlying these iterative adaptation algorithms is the minimal disturbance principle, which suggests that during training it is advisable to inject new information into a network in a manner that disturbs stored information to the smallest extent possible. The two principal kinds of online rules that have developed for altering the weights of a network are examined for both single-threshold elements and multielement networks. They are error-correction rules, which alter the weights of a network to correct error in the output response to the present input pattern, and gradient rules, which alter the weights of a network during each pattern presentation by gradient descent with the objective of reducing mean-square error (averaged over all training patterns).>

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Metadata

Authors
Widrow, B., & Lehr, M. A.
Venue
Proceedings of the IEEE
Published
1990
Volume
78
Issue
9
Pages
1415-1442
ISSN
0018-9219 (ISSN-L)
OpenAlex
W2116424792
Cited by
2,242
References
13
FWCI
43.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation. https://app.cochat.ai/papers/10.1109/5.58323
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Widrow, B., & Lehr, M. A. (1990). 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation. Proceedings of the IEEE. https://doi.org/10.1109/5.58323

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