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

journal contribution
posted on 2013-10-01, 00:00 authored by T H Lee, J H Park, O M Kwon, S M Lee
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.

History

Journal

Neural networks

Volume

46

Pagination

99 - 108

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0893-6080

eISSN

1879-2782

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Elsevier