Please use this identifier to cite or link to this item:
Title: Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays
Authors: Song, Q
Wang, Z
Keywords: Cohen–Grossberg neural networks;Stochastic neural networks;Exponential p-stability;Time-varying delays;Distributed delays;Impulsive effect
Issue Date: 2008
Publisher: Elsevier
Citation: Physica A: Statistical Mechanics and its Applications, 387(13): 3314-3326, May 2008
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.
Description: This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier Ltd
ISSN: 0378-4371
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf184.49 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.