### Abstract

A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N × N chessboard such that no pair of queens commands each other. The maximum rjeuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N^{2}-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

Original language | English |
---|---|

Pages (from-to) | 251-255 |

Number of pages | 5 |

Journal | Biological Cybernetics |

Volume | 76 |

Issue number | 4 |

Publication status | Published - 1997 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Biophysics

### Cite this

*Biological Cybernetics*,

*76*(4), 251-255.

**A maximum neural network approach for N-queens problems.** / Funabiki, Nobuo; Takenaka, Yoichi; Nishikawa, Seishi.

Research output: Contribution to journal › Article

*Biological Cybernetics*, vol. 76, no. 4, pp. 251-255.

}

TY - JOUR

T1 - A maximum neural network approach for N-queens problems

AU - Funabiki, Nobuo

AU - Takenaka, Yoichi

AU - Nishikawa, Seishi

PY - 1997

Y1 - 1997

N2 - A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N × N chessboard such that no pair of queens commands each other. The maximum rjeuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N2-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

AB - A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N × N chessboard such that no pair of queens commands each other. The maximum rjeuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N2-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

UR - http://www.scopus.com/inward/record.url?scp=0000055318&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0000055318&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0000055318

VL - 76

SP - 251

EP - 255

JO - Biological Cybernetics

JF - Biological Cybernetics

SN - 0340-1200

IS - 4

ER -