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
收藏
页码：509 / 521
页数：13
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