Stability of Time-Delay Systems
Stability of Time-Delay Systems
New Delay-Dependent Exponential Stability for Neural Networks With Time Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exponential Stability Analysis for Neural Networks With Time-Varying Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Network-based robust H∞ control of systems with uncertainty
Automatica (Journal of IFAC)
Global stability for cellular neural networks with time delay
IEEE Transactions on Neural Networks
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
A scaling parameter approach to delay-dependent state estimation of delayed neural networks
IEEE Transactions on Circuits and Systems II: Express Briefs
Extended state estimator design method for neutral-type neural networks with time-varying delays
International Journal of Systems, Control and Communications
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The problem of designing a globally exponentially convergent state estimator for a class of delayed neural networks is investigated in this paper. The time-delay pattern is quite general and including fast time-varying delays. The activation functions are monotone nondecreasing with known lower and upper bounds. A linear estimator of Luenberger-type is developed and by properly constructing a new Lyapunov-Krasovskii functional coupled with the integral inequality, the global exponential stability conditions of the error system are derived. The unknown gain matrix is determined by solving a delay-dependent linear matrix inequality. The developed results are shown to be less conservative than previously published ones in the literature, which is illustrated by a representative numerical example.