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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2015

01.04.2015 | Original Article

State estimation for memristor-based neural networks with time-varying delays

verfasst von: Hongzhi Wei, Ruoxia Li, Chunrong Chen

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2015

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Abstract

This paper is concerned with the state estimation problem for a class of memristor-based neural networks with time-varying delay. A delay dependent condition is developed to estimate the neuron states through observed output measurements such that the error system is globally asymptotically stable. By constructing more effective Lyapunov functionals, and combining with Jensen integral inequality and free-weighting matrix approach, a less conservative sufficient condition for the existence of state estimator is formulated in terms of linear matrix inequality, which can be checked efficiently by using some standard numerical packages. Finally, a numerical example is given to demonstrate the effectiveness of the presented results.
Vorheriger Artikel IF-preorder, IF-topology and IF-automata
Nächster Artikel Visual reasoning for discoveries

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Metadaten
Titel
State estimation for memristor-based neural networks with time-varying delays
verfasst von
Hongzhi Wei
Ruoxia Li
Chunrong Chen
Publikationsdatum
01.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0257-x

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