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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 8/2018

29-03-2017 | Original Article

Robust \(H_\infty\) filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays

Authors: Yajun Li, Feiqi Deng, Gai Li, Like Jiao

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2018

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Abstract

In this paper, the robust \(H_\infty\) filtering problem is discussed for a class of uncertain discrete-time stochastic neural networks with Markovian jumping parameters and mixed time-delays. Norm-bounded parameter uncertainties exist in both the state and measurement equation. The neuron activation function satisfies sector-bounded condition. The aim is to design a full-order filter with a prescribed \(H_\infty\) performance level. Delay-segment-dependent conditions are developed in terms of linear matrix inequalities (LMIs) such that the resulted filtering error systems robustly stochastically stable. Finally, example is provided to demonstrate the effectiveness and applicability of the related results are obtained in this paper.
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Metadata
Title
Robust filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays
Authors
Yajun Li
Feiqi Deng
Gai Li
Like Jiao
Publication date
29-03-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0651-2

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