Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays

Title
Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays
Author(s)
정호열락쉬마난박주현이태희R. Rakkiyappan[R. Rakkiyappan]
Keywords
GLOBAL EXPONENTIAL STABILITY; DISTRIBUTION-DEPENDENT STABILITY; ASYMPTOTIC STABILITY; NEUTRAL-TYPE; DISTRIBUTED DELAYS; ROBUST STABILITY; DISCRETE; TERM
Issue Date
201305
Publisher
ELSEVIER SCIENCE INC
Citation
APPLIED MATHEMATICS AND COMPUTATION, v.219, no.17, pp.9408 - 9423
Abstract
This paper is concerned with the stability criteria for bidirectional associative memory (BAM) neural networks with leakage time delay and probabilistic time-varying delays. By establishing a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delay is transformed into the deterministic time-varying delay with stochastic parameters. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, delay-probability-distribution-dependent sufficient conditions are derived to achieve the globally asymptotically mean square stable of the considered BAM neural networks. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results. (c) 2013 Elsevier Inc. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/25900http://dx.doi.org/10.1016/j.amc.2013.03.070
ISSN
0096-3003
Appears in Collections:
공과대학 > 모바일정보통신공학과 > Articles
공과대학 > 전기공학과 > Articles
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