Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays

Qiankun Song and Zidong Wang

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 13, 3314-3326

Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.

Keywords: Cohen–Grossberg neural networks; Stochastic neural networks; Exponential p-stability; Time-varying delays; Distributed delays; Impulsive effect (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:13:p:3314-3326

DOI: 10.1016/j.physa.2008.01.079

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