### 抜粋

The authors introduce a neural computation architecture based on a stochastic Hopfield neural network model for solving job-shop scheduling. A computation circuit computes the total completion times (costs) of all jobs, and the cost difference is added to the energy function of the stochastic neural network. Using a simulated annealing algorithm, the temperature of the system is slowly decreased according to an annealing schedule until the energy of the system is at a local or global minimum. By choosing an appropriate annealing schedule, near-optimal and optimal solutions to job-shop problems can be found. The architecture of the system is diagrammed at both the functional and circuit levels. Simulation results are presented.

元の言語 | English |
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ページ | 283-290 |

ページ数 | 8 |

出版物ステータス | Published - 1988 12 1 |

外部発表 | Yes |

### ASJC Scopus subject areas

- Engineering(all)

## フィンガープリント Stochastic neural networks for solving job-shop scheduling. II - Architecture and simulations' 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

*Stochastic neural networks for solving job-shop scheduling. II - Architecture and simulations*. 283-290.