The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
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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Cites
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Self-citationAdaptive noise cancelling: Principles and applications
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Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Causability and explainability of artificial intelligence in medicine
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What do we need to build explainable AI systems for the medical domain?
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Neural networks: applications in industry, business and science
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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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