Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

Liangliang Li and Jigui Jian

Applied Mathematics and Computation, 2015, vol. 266, issue C, 860-873

Abstract: This paper is concerned with the problem of global exponential p-convergence for stochastic BAM neural networks with time-varying and infinite distributed delays. By constructing a new delay differential-integral inequality and a novel L-operator differential-integral inequality, and coupling with stochastic analysis techniques, some delay-dependent sufficient conditions are derived to guarantee exponential p-convergence and the state variables of the discussed stochastic BAM neural networks are globally exponentially convergent to a ball in the state space with a pre-specified convergence rate. Meanwhile, the exponential p-convergent balls are also estimated. Here, the existence and the uniqueness of the equilibrium point needs not to be considered. Finally, two examples with numerical simulations are given to illustrate the effectiveness of the theoretical results.

Keywords: Stochastic BAM neural networks; Exponential p-convergence; Infinite distributed delays; L-operator differential-integral inequality; Ito^’s formula (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:266:y:2015:i:c:p:860-873

DOI: 10.1016/j.amc.2015.06.022

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