An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain

Yoshiyasu Takefuji, Kuo Chun Lee, Hideo Also

研究成果: Article

69 引用 (Scopus)

抜粋

A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.

元の言語English
ページ(範囲)243-251
ページ数9
ジャーナルBiological Cybernetics
67
発行部数3
DOI
出版物ステータスPublished - 1992 7 1
外部発表Yes

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)

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