New delay-dependent robust stability criterion for neutral stochastic neural networks with time delays

In this paper, the problem of delay-dependent robust stability for uncertain neutral stochastic neural networks with time delays is considered. Based on Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, some new delay-dependent stability conditions in terms of LMI...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1145 - 1149
Main Authors Qiu-Ji Qing, Hai-Kuo He, Duo-Ming Xi, Ji-Yong Lu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Cybernetics
Delay effects
delay-dependent
Machine learning
Neural networks
Neutral
Robust stability
stochastic
Stochastic processes
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Summary:In this paper, the problem of delay-dependent robust stability for uncertain neutral stochastic neural networks with time delays is considered. Based on Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, some new delay-dependent stability conditions in terms of LMIs are derived by introducing some free weighting matrices and using Leibniz-Newton formula which can be selected properly to lead to less conservative results. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the results.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212419