### 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 |
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ページ（範囲） | 243-251 |

ページ数 | 9 |

ジャーナル | Biological Cybernetics |

巻 | 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)

## フィンガープリント An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

## これを引用

*Biological Cybernetics*,

*67*(3), 243-251. https://doi.org/10.1007/BF00204397