Modified Hopfield-Tank Neural Networks Applied to the “Unitized” Maximum Flow Problem

Toshinori Munakata, Yoshiyasu Takefuji, Henrik Johansson

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)174-177
ページ数4
ジャーナルIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
41
2
DOI
出版ステータスPublished - 1994 2
外部発表はい

ASJC Scopus subject areas

  • 電子工学および電気工学

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