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Neural Computing and Applications
Published in: Neural Computing and Applications 18/2020

11-03-2020 | Original Article

State estimation of T–S fuzzy Markovian generalized neural networks with reaction–diffusion terms: a time-varying nonfragile proportional retarded sampled-data control scheme

Authors: Xiaona Song, Jingtao Man, Shuai Song, Zhen Wang

Published in: Neural Computing and Applications | Issue 18/2020

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Abstract

This paper focuses on the state estimation issue of T–S fuzzy Markovian generalized neural networks (GNNs) with reaction–diffusion terms. An estimator-based nonfragile time-varying proportional retarded sampled-data controller that permits norm-bounded indeterminacy and contains a time-varying delay is designed to guarantee the asymptotical stability of the error system. By establishing a novel Lyapunov–Krasovskii functional that involves positive indefinite items and discontinuous items, meanwhile, by combining the reciprocally convex combination method, Jenson’s inequality and Wirtinger inequality, a less conservative stability criterion can be derived. Moreover, the principle for the number of selected variables in the process of deriving main results is also analyzed. Finally, two numerical examples are given to demonstrate the validity and advantages of the results proposed in this paper.
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Appendix
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Metadata
Title
State estimation of T–S fuzzy Markovian generalized neural networks with reaction–diffusion terms: a time-varying nonfragile proportional retarded sampled-data control scheme
Authors
Xiaona Song
Jingtao Man
Shuai Song
Zhen Wang
Publication date
11-03-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 18/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04817-7

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