Stability of Time-Delay Systems
Stability of Time-Delay Systems
Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Almost sure exponential stability of recurrent neural networks with Markovian switching
IEEE Transactions on Neural Networks
Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Convergence of a Subclass of Cohen–Grossberg Neural Networks via the Łojasiewicz Inequality
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New Delay-Dependent Exponential Stability for Neural Networks With Time Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
State estimation of markovian jump neural networks with mixed time delays
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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The state estimation problem is studied in this paper for a class of recurrent neural networks with time-varying delay. A novel delay partition approach is developed to derive a delay-dependent condition guaranteeing the existence of a desired state estimator for the delayed neural networks. The design of the gain matrix of the state estimator can be achieved by solving a linear matrix inequality, where no slack variable is involved. A numerical example is finally provided to show the advantage of the proposed approach over some existing results.