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Erschienen in: International Journal of Machine Learning and Cybernetics 3/2017

10.05.2015 | Original Article

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

verfasst von: Mingang Hua, Huasheng Tan, Juntao Fei

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2017

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Abstract

The state estimation problem is considered for a class of discrete-time stochastic neural networks with Markovian jumping parameters in this paper. Norm-bounded parameter uncertainties in the state and measurement equation and time-varying delays are investigated. The neuron activation function satisfies sector-bounded conditions, and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sector-bounded conditions. By constructing proper Lyapunov–Krasovskii functional, delay-dependent conditions are developed in terms of linear matrix inequalities (LMIs) to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable. Finally, numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design method.

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Metadaten
Titel
State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays
verfasst von
Mingang Hua
Huasheng Tan
Juntao Fei
Publikationsdatum
10.05.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0373-2

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