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Stabilization of stochastic delayed neural networks with Markovian switching
Author(s) -
Sun Yonghui,
Cao Jinde
Publication year - 2008
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.26
Subject(s) - control theory (sociology) , exponential stability , artificial neural network , stochastic neural network , controller (irrigation) , exponential growth , markov process , set (abstract data type) , stability (learning theory) , state (computer science) , mathematics , computer science , control (management) , recurrent neural network , algorithm , nonlinear system , artificial intelligence , physics , statistics , mathematical analysis , quantum mechanics , machine learning , agronomy , biology , programming language
In this paper, the mean square exponential stabilization problem is investigated for a class of stochastic delayed neural networks with Markovian switching. After proposing an exponential stability condition, our attention is focused on the design of a state feedback controller such that the stochastic delayed neural networks with Markovian switching is exponentially stable in mean square. Several stabilization criteria, delay‐independent and delay‐dependent ones, which are expressed in terms of a set of linear matrix inequalities (LMIs), are proposed to stabilize the stochastic delayed neural networks with Markovian switching exponentially. The usefulness and applicability of the developed results are illustrated by means of two numerical examples. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society