Exponential stability for stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays

Authors:
Yuanhua Du;Shouming Zhong;Nan Zhou;Kaibo Shi;Jun Cheng
Affiliations:
-;-;-;-;-
Venue:
Neurocomputing
Year:
2014

Citing 6
Cited 0

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Robust stability of uncertain fuzzy Cohen-Grossberg BAM neural networks with time-varying delays

Expert Systems with Applications: An International Journal
Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: An LMI approach

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Exponential synchronization of stochastic Cohen-Grossberg neural networks with mixed time-varying delays and reaction-diffusion via periodically intermittent control

Neural Networks
Stability analysis of Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays

Mathematical and Computer Modelling: An International Journal

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Abstract

This paper considers the issue of exponential stability analysis for stochastic Cohen-Grossberg BAM (SCGBAM) neural networks with discrete and distributed time-varying delays. The exponential stability criteria are proposed by applying stochastic analysis theory and establishing a new Lyapunov-Krasovskii functional. A set of novel sufficient conditions is obtained to guarantee the exponential stability of stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays. The several exponential stability criteria proposed in this paper are simpler and effective. Finally, two numerical examples are provided to demonstrate the low conservatism and effectiveness of the proposed results.