Generalized neural network model

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

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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
StatePublished - 1988
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: Sep 6 1988Sep 10 1988

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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