Global exponential stability of impulsive neural networks with variable delay: an LMI approach
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Technical communique: An improved result for complete stability of delayed cellular neural networks
Automatica (Journal of IFAC)
Some extensions of a new method to analyze complete stability of neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay
IEEE Transactions on Neural Networks
Improved Delay-Dependent Asymptotic Stability Criteria for Delayed Neural Networks
IEEE Transactions on Neural Networks
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This paper presents new complete stability results for delayed cellular neural networks (DCNNs). A novel method is proposed for complete stability analysis of DCNNs. By applying the M-matrix theory and introducing some new estimation techniques on the solutions of DCNNs, a simple and improved complete stability criterion is derived. The new criterion unifies the delay-dependent and delay-independent complete stability conditions for DCNNs. Moreover, the obtained delay-dependent criterion can give a larger upper bound of the time delay than the existing ones such that the complete stability can still be retained. Numerical examples are presented which show that the new complete stability results for DCNNs are compared favorably with the existing results.