Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108672
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Type: Conference paper
Title: Delay-dependent exponential stability for neutral stochastic Markov neural networks with time-varying delay
Author: Chen, H.
Shi, P.
Lim, C.
Citation: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2015 / pp.719-724
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISBN: 9781479986972
ISSN: 0884-3627
Conference Name: IEEE International Conference on Systems, Man, and Cybernetics (SMC) (09 Oct 2015 - 12 Oct 2015 : Hong Kong)
Statement of
Responsibility: 
Huabin Chen, Peng Shi, Cheng-Chew Lim
Abstract: In this article, stability analysis for neutral stochastic neural networks with time-varying delay and Markovian jumping parameters is studied. By utilizing the theory of functional differential equations, some results about exponential stability criteria in p (p > 1)-moment, which are delay-dependent, are given. The primary advantages of these obtained results over some recent and similar works are that the differentiability or continuity of the delay function is not required, and that the difficulty stemming from the existence of the neutral item is overcome. A numerical example is given to examine the correctness of the derived result.
Description: Big Data Analytics for Human-Centric Systems
Rights: Copyright © 2015 by The Institute of Electrical and Electronics Engineers, Inc.
RMID: 0030043997
DOI: 10.1109/SMC.2015.135
Appears in Collections:Electrical and Electronic Engineering publications

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