IEEE Transactions on Circuits and Systems II: Express Briefs
Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
Exponential Stability Analysis for Neural Networks With Time-Varying Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses
Neural Processing Letters
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Uniqueness and robust exponential stability are analyzed for a class of uncertain cellular neural networks with time-varying delays. By dividing the variation interval of the time delay into two subintervals with equal length, a novel Lyapunov-Krasovskii functional is introduced. Using the free-weighting matrix method, a new delay-dependent stability criterion is obtained, which is less conservative than some previous literature. Since the result is presented in terms of linear matrix inequalities, the condition is easy to be verified. Finally, an example is given to illustrate the effectiveness of our proposed method.