Stability in distribution of stochastic delay recurrent neural networks with Markovian switching

被引：8

作者：

Zhu, Enwen ^{[1
]}

Yin, George ^{[2
]}

Yuan, Quan ^{[3
]}

机构：

[1] Changsha Univ Sci & Technol, Sch Math & Comp Sci, Changsha 410004, Hunan, Peoples R China

[2] Wayne State Univ, Dept Math, Detroit, MI 48202 USA

[3] Univ Wisconsin Stout, Dept Math Stat & Comp Sci, Menomonie, WI 54751 USA

来源：

NEURAL COMPUTING & APPLICATIONS | 2016年 / 27卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Stability in distribution; Stochastic recurrent neural network; Brownian motion; Markov chain; TIME-VARYING DELAYS; EXPONENTIAL STABILITY; DIFFERENTIAL-EQUATIONS; STATE ESTIMATION; ACTIVATION FUNCTIONS; ERGODICITY; PARAMETERS; JUMP;

D O I：

10.1007/s00521-015-2013-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the stability in distribution of stochastic delay recurrent neural networks with Markovian switching. Using Lyapunov function and stochastic analysis techniques, sufficient conditions on the stability in distribution are given. For such recurrent neural networks, it reveals that the limit distribution of transition probability for segment process associated with solution process is indeed a unique ergodic invariant probability measure. Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.

引用

页码：2141 / 2151

页数：11

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