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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2016

01.08.2016 | Original Article

Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions

verfasst von: Huaiqin Wu, Leifei Wang, Yu Wang, Peifeng Niu, Bolin Fang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2016

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Abstract

This paper is concerned with the exponential state estimation issue for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions. By introducing triple-integral terms and quadruple integrals term in Lyapunov–Krasovskii functional, the obtained Lyapunov matrices are distinct for different system modes. Based on the nonsmooth analysis theory and by applying stochastic analysis techniques, the full-order state estimator is designed to ensure that the corresponding error system is exponentially stable in mean square. The desired mode-dependent and delay-dependent estimator can be achieved by solving a set of linear matrix inequalities. Finally, two simulation examples are given to illustrate the validity of the theoretical results.
Vorheriger Artikel On active annotation for named entity recognition
Nächster Artikel Interval-valued fuzzy planar graphs

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Metadaten
Titel
Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions
verfasst von
Huaiqin Wu
Leifei Wang
Yu Wang
Peifeng Niu
Bolin Fang
Publikationsdatum
01.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0447-1

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