Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay |
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| Authors: | Wang Shen-Quan Feng Jian and Zhao Qing |
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| Affiliation: | College of Information Science and Engineering,Northeastern University;Department of Electrical and Computer Engineering,University of Alberta |
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| Abstract: | In this paper,the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks(CRNNs) with stochastic delay.Different from the common assumptions on time delays,it is assumed that the probability distribution of the delay taking values in some intervals is known a priori.By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique(the reciprocally convex combination method),less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities(LMIs).Two numerical examples show that our results are better than the existing ones. |
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| Keywords: | recurrent neural networks stochastic delay mean-square stability linear matrix inequality |
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