Adaptive synchronization of Cohen–Grossberg neural network with mixed time-varying delays and stochastic perturbation

Chaolong Zhang, Feiqi Deng, Yunjian Peng and Bo Zhang

Applied Mathematics and Computation, 2015, vol. 269, issue C, 792-801

Abstract: In this paper, based on the LaSalle invariant principle of stochastic differential delay equations and the stochastic analysis theory as well as the adaptive feedback technique, several sufficient conditions ensuring the adaptive synchronization of Cohen–Grossberg neural network with mixed time-varying delays and stochastic perturbation are derived. In particular, the synchronization criterion considered globally is the almost surely asymptotic stability of the error dynamical system. Our synchronization criterion is easily verified and does not solve any linear matrix inequality. These results generalized a few previous known results. At last, a numerical example and its simulations are provided to demonstrate the effectiveness and advantage of the theoretical results.

Keywords: Adaptive synchronization; Cohen–Grossberg neural network; Mixed time-varying delays; Stochastic perturbation; LaSalle invariant principle; Adpative feedback control (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:269:y:2015:i:c:p:792-801

DOI: 10.1016/j.amc.2015.07.074

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