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.
|出版ステータス||Published - 1989 12 1|
|イベント||IJCNN International Joint Conference on Neural Networks - Washington, DC, USA|
継続期間: 1989 6 18 → 1989 6 22
|Other||IJCNN International Joint Conference on Neural Networks|
|City||Washington, DC, USA|
|Period||89/6/18 → 89/6/22|
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