Journal
COMPLEXITY
Volume 20, Issue 3, Pages 39-65Publisher
WILEY-HINDAWI
DOI: 10.1002/cplx.21503
Keywords
bidirectional associative memory neural networks; global exponential stability; Markovian jumping parameters; Probabilistic time-varying delays
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2013R1A1A2A10005201]
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In this article, an exponential stability analysis of Markovian jumping stochastic bidirectional associative memory (BAM) neural networks with mode-dependent probabilistic time-varying delays and impulsive control is investigated. By establishment of a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and stochastic parameters. By fully taking the inherent characteristic of such kind of stochastic BAM neural networks into account, a novel Lyapunov-Krasovskii functional is constructed with as many as possible positive definite matrices which depends on the system mode and a triple-integral term is introduced for deriving the delay-dependent stability conditions. Furthermore, mode-dependent mean square exponential stability criteria are derived by constructing a new Lyapunov-Krasovskii functional with modes in the integral terms and using some stochastic analysis techniques. The criteria are formulated in terms of a set of linear matrix inequalities, which can be checked efficiently by use of some standard numerical packages. Finally, numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results. (c) 2014 Wiley Periodicals, Inc. Complexity 20: 39-65, 2015
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