抄録
A parallel and stochastic version of Hopfield-like neural networks is presented. Cauchy color noise is assumed. The specific noise is desirable for fast convergence to a fixed point representing a neighborhood minimum. It can be quickly quenched at each iteration according to a proven cooling schedule in generating random states on the energy landscape. An exact Cauchy acceptance criterion is analytically derived for hill-climbing capability. The improvement is twofold: a faster cooling schedule (the inversely linear cooling schedule characterized by the Cauchy simulated annealing) and parallel executions of all neurons. Such a Cauchy machine can be electronically implemented, and the design is given.
| 本文言語 | English |
|---|---|
| ページ | 529-532 |
| ページ数 | 4 |
| 出版ステータス | Published - 1989 12月 1 |
| 外部発表 | はい |
| イベント | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA 継続期間: 1989 6月 18 → 1989 6月 22 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
|---|---|
| City | Washington, DC, USA |
| Period | 89/6/18 → 89/6/22 |
ASJC Scopus subject areas
- 工学（全般）