Stochastic sampled-data control for state estimation of time-varying delayed neural networks

Authors:
Tae H. Lee;Ju H. Park;O. M. Kwon;S. M. Lee
Affiliations:
-;-;-;-
Venue:
Neural Networks
Year:
2013

Citing 21
Cited 1

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Stabilization of Nonlinear Systems Using Sampled-Data Output-Feedback Fuzzy Controller Based on Polynomial-Fuzzy-Model-Based Control Approach

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Abstract

This study examines the state estimation problem for neural networks with a time-varying delay. Unlike other studies, the sampled-data with stochastic sampling is used to design the state estimator using a novel approach that divides the bounding of the activation function into two subintervals. To fully use the sawtooth structure characteristics of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. The desired estimator gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Finally, the proposed method is applied to two numerical examples to show the effectiveness of our result.