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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2018

12-10-2016 | Original Article

\(H_{\infty }\) filter design for delayed static neural networks with Markovian switching and randomly occurred nonlinearity

Authors: Yaling Cheng, Mingang Hua, Pei Cheng, Fengqi Yao, Juntao Fei

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2018

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Abstract

The paper is concerned with the problem of \(H_{\infty }\) filter design for delayed static neural networks with Markovian switching and randomly occurred nonlinearity. The random phenomenon is described in terms of a Bernoulli stochastic variable. Based on the reciprocally convex approach, a lower bound lemma is proposed to handle the double- and triple-integral terms in the time derivative of the Lyapunov function. Finally, the optimal performance index is obtained via solving linear matrix inequalities(LMIs). The result is not only less conservative but the time derivative of the time delay can be greater than one. Numerical examples with simulation results are provided to illustrate the effectiveness of the developed results.
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Metadata
Title
filter design for delayed static neural networks with Markovian switching and randomly occurred nonlinearity
Authors
Yaling Cheng
Mingang Hua
Pei Cheng
Fengqi Yao
Juntao Fei
Publication date
12-10-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0613-0

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