Extended state estimator design method for neutral-type neural networks with time-varying delays
by Magdi Sadek Mahmoud
International Journal of Systems, Control and Communications (IJSCC), Vol. 4, No. 1/2, 2012

Abstract: The problem of designing a state estimator having a global exponential convergence for a class of delayed neural networks of neutral-type is investigated in this paper. The time-delay pattern is a bounded differentiable time-varying function. The activation functions are globally Lipschitz. 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 previous published ones in the literature, which is illustrated by a representative numerical example.

Online publication date: Sat, 23-Aug-2014

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