Modified Hopfield-Tank neural networks applied to the 'unitized' maximum flow problem

Toshinori Munakata, Yoshiyasu Takefuji, Henrik Johansson

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Two new approaches called 'graph unitization' are proposed to apply neural networks similar to the Hopfield-Tank models to determine optimal solutions for the maximum flow problem. They are: (1) n-vertex and n2-edge neurons on a unitized graph; (2) m-edge neurons on a unitized graph. Graph unitization is to make the flow capacity of every edge equal to 1 by placing additional vertices or edges between existing vertices. In our experiments, solutions converged most of the time, and the converged solutions were always optimal, rather than near optimal.

Original languageEnglish
Pages (from-to)174-177
Number of pages4
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume41
Issue number2
DOIs
Publication statusPublished - 1994 Feb
Externally publishedYes

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Neurons
Neural networks
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Modified Hopfield-Tank neural networks applied to the 'unitized' maximum flow problem. / Munakata, Toshinori; Takefuji, Yoshiyasu; Johansson, Henrik.

In: IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 41, No. 2, 02.1994, p. 174-177.

Research output: Contribution to journalArticle

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