Generalized neural network model

X. Xu, W. T. Tsai, N. K. Huang

Research output: Contribution to journalArticle

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Abstract

This paper proposes a neural network model, which generalizes McCulloch-Pitts' model (McCulloch and Pitts, 1943) and Hopfield's model (Hopfield, 1982, 1984). We prove that the generalized model converges and has all the desirable properties of both McCulloch-Pitts' model and Hopfield's model. It is easier and more natural to formulate some application problems with the proposed class of models than with McCulloch-Pitts' or Hopfield's models, e.g., the 3-Satisfiability problem. The continuous counterpart of the binary model is provided too. Also, single-attributed neuron model is extended to multi-attributed neuron model in both binary and continuous cases.

Original languageEnglish (US)
Pages (from-to)150
Number of pages1
JournalNeural Networks
Volume1
Issue number1 SUPPL
DOIs
Publication statusPublished - 1988
Externally publishedYes

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Neuroscience(all)

Cite this

Xu, X., Tsai, W. T., & Huang, N. K. (1988). Generalized neural network model. Neural Networks, 1(1 SUPPL), 150. https://doi.org/10.1016/0893-6080(88)90189-X