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

journal contribution
posted on 2013-05-01, 00:00 authored by Lakshmanan Shanmugam, J H Park, T H Lee, H Y Jung, R Rakkiyappan
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.

History

Journal

Applied mathematics and computation

Volume

219

Issue

17

Pagination

9408 - 9423

Publisher

Elsevier

Location

Philadelphia, Pa.

ISSN

0096-3003

eISSN

1873-5649

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Elsevier