Quantized passive filtering for switched delayed neural networks
Articles
Youmei Zhou
Anhui University of Technology
https://orcid.org/0000-0002-5709-1551
Yajuan Liu
North China Electric Power University
Jianping Zhou
Anhui University of Technology
Zhen Wang
Shandong University of Science and Technology
Published 2021-01-01
https://doi.org/10.15388/namc.2021.26.20562
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Keywords

quantization
passive filter
arbitrary switching
semi-Markov switching

How to Cite

Zhou Y., Liu Y., Zhou J. and Wang Z. (2021) “Quantized passive filtering for switched delayed neural networks”, Nonlinear Analysis: Modelling and Control, 26(1), pp. 93-112. doi: 10.15388/namc.2021.26.20562.

Abstract

The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods.

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