Discussion of Stability on Recurrent Neural Networks for Nonlinear Dynamic Systems

被引：0

作者：

Liu Lisang ^{[1
]}

Peng Xiafu ^{[2
]}

机构：

[1] Fujian Univ Technol, Dept Elect Informat & Elect Engn, Fuzhou, Fujian, Peoples R China

[2] Xiamen Univ, Dept Automat, Xiamen, Fujian, Peoples R China

来源：

PROCEEDINGS OF 2012 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION, VOLS I-VI | 2012年

关键词：

stability; diagonal recurrent neural network; nonlinear dynamic system;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Stability analysis is a most important problem in the dynamic analysis of dynamical systems. The stability properties and dynamic behavior of the recurrent neural network for nonlinear dynamic system modeling directly determine its engineering applications. In this paper, based on Lyapunov stability theory, the stability problems of recurrent neural networks (RNN) and its general stability conditions are discussed. And a novel diagonal recurrent neural network with output feedback (O-DRNN) is proposed as an concrete example, analyzing its stability as well as the range of learning rate.

引用

页码：142 / 145

页数：4

共 8 条

[1]

[Anonymous], 1960, DESIGN BRAIN

[2]

[Anonymous], NEURAL NETWORKS, DOI DOI 10.1016/0893-6080(89)90020-8

[3] REPRESENTATIONS OF NON-LINEAR SYSTEMS - THE NARMAX MODEL [J].

CHEN, S ;

BILLINGS, SA .

INTERNATIONAL JOURNAL OF CONTROL, 1989, 49 (03) :1013-1032

[4] DIAGONAL RECURRENT NEURAL NETWORKS FOR DYNAMIC-SYSTEMS CONTROL [J].

KU, CC ;

LEE, KY .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1995, 6 (01) :144-156

[5]

KU CC, 1992, IEEE C NEURAL NETW, V3, P839

[6]

Liao X., 1999, THEORY METHODS APPL

[7]

Liu L. S., 2011, INT C COMPUTER SCI E, V6, P286

[8]

Zhang Xiao-shuang, 2002, Journal of System Simulation, V14, P641

← 1 →