Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/83102
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Type: Journal article
Title: Exponential stability on stochastic neural networks with discrete interval and distributed delays
Author: Yang, R.
Zhang, Z.
Shi, P.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2010; 21(1):169-175
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2010
ISSN: 1045-9227
1941-0093
Statement of
Responsibility: 
Rongni Yang, Zexu Zhang, and Peng Shi
Abstract: This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 < d1 ?? d(t) ?? d2 and is described as d(t) = d 1+h(t) with 0 ?? h(t) ?? d 2 - d 1. Based on the idea of partitioning the lower bound d 1, new delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.
Keywords: Humans
Stochastic Processes
Nonlinear Dynamics
Time Factors
Computer Simulation
Information Storage and Retrieval
Neural Networks, Computer
Rights: © 2009 IEEE
DOI: 10.1109/TNN.2009.2036610
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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