Generalized neural network model

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

doi:10.1016/0893-6080(88)90189-X

Generalized neural network model

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

Computing and Augmented Intelligence, School of (IAFSE-SCAI)

Research output: Contribution to journal › Conference article › peer-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 language	English (US)
Pages (from-to)	150
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90189-X
State	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: Sep 6 1988 → Sep 10 1988

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

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10.1016/0893-6080(88)90189-X

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title = "Generalized neural network model",

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.",

author = "X. Xu and Tsai, {W. T.} and Huang, {N. K.}",

year = "1988",

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language = "English (US)",

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T1 - Generalized neural network model

AU - Xu, X.

AU - Tsai, W. T.

AU - Huang, N. K.

PY - 1988

Y1 - 1988

N2 - 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.

AB - 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.

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U2 - 10.1016/0893-6080(88)90189-X

DO - 10.1016/0893-6080(88)90189-X

M3 - Conference article

AN - SCOPUS:0024167478

SN - 0893-6080

VL - 1

SP - 150

JO - Neural Networks

JF - Neural Networks

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -

Generalized neural network model

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