Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Stability of Time-Delay Systems
Stability of Time-Delay Systems
International Journal of Systems Science
A new criterion for global robust stability of interval delayed neural networks
Journal of Computational and Applied Mathematics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Delayed Cellular Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays
IEEE Transactions on Neural Networks
New Delay-Dependent Stability Results for Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
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This paper is concerned with the exponential stability problem for a class of stochastic Cohen---Grossberg neural networks with discrete and unbounded distributed time delays. By applying the Jensen integral inequality and the generalized Jensen integral inequality, several improved delay-dependent criteria are developed to achieve the exponential stability in mean square in terms of linear matrix inequalities. It is established theoretically that two special cases of the obtained criteria are less conservative than some existing results but including fewer slack variables. As the present conditions involve fewer free weighting matrices, the computational burden is largely reduced. Three numerical examples are provided to demonstrate the effectiveness of the theoretical results.