HYSTERESIS IN NEURAL NETWORKS

被引：3

作者：

SHUKLA, P

SINHA, TK

机构：

[1] Physics Department, North Eastern Hill University

来源：

PHYSICAL REVIEW E | 1994年 / 49卷 / 06期

关键词：

D O I：

10.1103/PhysRevE.49.R4811

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

摘要：

The zero-temperature dynamics of neural networks is used to study history-dependent effects in random magnets. Random-field as well as random-bond Ising models are shown to exhibit hysteresis. The hysteresis loops of the random-bond model have a staircase structure while those of the random-field model are relatively smooth. We discuss the relevance of neural networks in the context of random-bond models, and the bearing the neural network dynamics has on the spectrum of relaxation times in a hysteretic system.

引用

页码：R4811 / R4814

页数：4

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