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Strictly intermittent quantized control for fixed/predefined-time cluster lag synchronization of stochastic multi-weighted complex networks.
Qin, Xuejiao; Jiang, Haijun; Qiu, Jianlong; Hu, Cheng; Ren, Yue.
Afiliação
  • Qin X; College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, PR China.
  • Jiang H; College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, PR China. Electronic address: jianghaijunxju@163.com.
  • Qiu J; School of Automation and Electrical Engineering, Linyi University, Linyi 276005, PR China.
  • Hu C; College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, PR China.
  • Ren Y; College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, PR China.
Neural Netw ; 158: 258-271, 2023 Jan.
Article em En | MEDLINE | ID: mdl-36481458
This article addresses the fixed-time (F-T) and predefined-time (P-T) cluster lag synchronization of stochastic multi-weighted complex networks (SMWCNs) via strictly intermittent quantized control (SIQC). Firstly, by exploiting mathematical induction and reduction to absurdity, a novel F-T stability lemma is proved and an accurate estimation of settling time (ST) is obtained. Subsequently, by virtue of the proposed F-T stability, some simple conditions that ensure the F-T cluster lag synchronization of SMWCNs are derived by developing a SIQC strategy. Furthermore, the P-T cluster lag synchronization is also explored based on a SIQC design, where the ST can be predefined by an adjustable constant of the controller. Note that the designed controllers here are simpler and more economical than the traditional design whose the linear part is still activated during the rest interval. Finally, two numerical examples are provided to verify the effectiveness of the theoretical results.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Idioma: En Ano de publicação: 2023 Tipo de documento: Article