Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/78397
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Type: Journal article
Title: Dissipativity analysis for discrete-time stochastic neural networks with time-varying delays
Author: Wu, Z.
Shi, P.
Su, H.
Chu, J.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2013; 24(3):345-355
Publisher: IEEE
Issue Date: 2013
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu
Abstract: In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly $({Q},{S},{R}){hbox{-}}gamma$-dissipative, which is delay-dependent in the sense that it depends on not only the discrete delay but also the finite-distributed delay. Based on the dissipativity criterion, some special cases are also discussed. Compared with the existing ones, the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables. Three examples are given to illustrate the effectiveness and benefits of our theoretical results.
Keywords: Delay-dependent; dissipativity; neural networks; stochastic systems; time-delays
Rights: © 2013 IEEE
RMID: 0020125682
DOI: 10.1109/TNNLS.2012.2232938
Appears in Collections:Electrical and Electronic Engineering publications

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