Solubility and stability of recurrent neural networks with nonlinearity or time-varying delays

被引：3

作者：

Yen, Eva C. ^{[1
]}

机构：

[1] Natl Cent Univ, Dept Business Adm, Jhongli 320, Taoyuan County, Taiwan

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2011年 / 16卷 / 01期

关键词：

Recurrent neural network; Asymptotically stable; A noisy time series; Nonlinearities; Time-varying delays;

D O I：

10.1016/j.cnsns.2010.04.024

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Because to apply a deterministic RNN to a noisy time series and the existence of a linear approximation are doubtful, we reconsider the solubility and stability of a recurrent neural network (RNN). Simpler methods are proposed to replace the complicated nonsingular M-matrix method when nonlinearities exist and to replace the complicated linear matrix inequality method when time-varying delays exist. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：509 / 521

页数：13

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