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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2021

18.01.2021

Quantized Sampled-Data Control for Exponential Stabilization of Delayed Complex-Valued Neural Networks

verfasst von: Xiaohong Wang, Zhen Wang, Jianwei Xia, Qian Ma

Erschienen in: Neural Processing Letters | Ausgabe 2/2021

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Abstract

This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. Based on the discrete-time Lyapunov stability theory, reciprocally convex approach, a sector bound approach, and some estimation techniques, a reduced conservative stabilization criterion is obtained to guarantee the exponential stabilization of the considered CVNNs. The desired quantized sampled-data controller is designed via converting the complex-valued linear matrix inequality into real-valued ones. The effectiveness of the derived criteria are shown via an illustrative simulation example.
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Metadaten
Titel
Quantized Sampled-Data Control for Exponential Stabilization of Delayed Complex-Valued Neural Networks
verfasst von
Xiaohong Wang
Zhen Wang
Jianwei Xia
Qian Ma
Publikationsdatum
18.01.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10422-5

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