Abstract
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.
| Original language | English |
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| Pages | 529-532 |
| Number of pages | 4 |
| Publication status | Published - 1989 Dec 1 |
| Externally published | Yes |
| Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: 1989 Jun 18 → 1989 Jun 22 |
Other
| Other | IJCNN International Joint Conference on Neural Networks |
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| City | Washington, DC, USA |
| Period | 89/6/18 → 89/6/22 |
ASJC Scopus subject areas
- Engineering(all)