Neural Networks2022,Vol.1499.DOI:10.1016/j.neunet.2022.02.013

Finite-time and sampled-data synchronization of complex dynamical networks subject to average dwell-time switching signal

Gunasekaran, Nallappan Ali, M. Syed Arik, Sabri Ghaffar, H. I. Abdul Diab, Ahmed A. Zaki
Neural Networks2022,Vol.1499.DOI:10.1016/j.neunet.2022.02.013

Finite-time and sampled-data synchronization of complex dynamical networks subject to average dwell-time switching signal

Gunasekaran, Nallappan 1Ali, M. Syed 2Arik, Sabri 3Ghaffar, H. I. Abdul 4Diab, Ahmed A. Zaki5
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作者信息

  • 1. Computat Intelligence Lab,Toyota Technol Inst
  • 2. Dept Math,Thiruvalluvar Univ
  • 3. Fac Engn,Istanbul Univ Cerrahpasa
  • 4. Egyptian Korean Fac Ind & Energy Technol,Banu Suef Technol Univ
  • 5. Dept Elect Engn,Minia Univ
  • 折叠

Abstract

This study deals with the finite-time synchronization problem of a class of switched complex dynamical networks (CDNs) with distributed coupling delays via sampled-data control. First, the dynamical model is studied with coupling delays in more detail. The sampling system is then converted to a continuous time-delay system using an input delay technique. We obtain some unique and less conservative criteria on exponential stability using the Lyapunov-Krasovskii functional (LKF), which is generated with a Kronecker product, linear matrix inequalities (LMIs), and integral inequality. Furthermore, some sufficient criteria are derived by an average dwell-time method and determine the finite-time boundedness of CDNs with switching signal. The proposed sufficient conditions can be represented in the form of LMIs. Finally, numerical examples are given to show that the suggested strategy is feasible. (c) 2022 Elsevier Ltd. All rights reserved.

Key words

Average dwell-time/Complex dynamical networks/Finite-time boundedness/Lyapunov method/Sampled-data control/Switching signal/RECURRENT NEURAL-NETWORKS/VARYING DELAYS/SYSTEMS/PASSIVITY/CRITERIA

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出版年

2022
Neural Networks

Neural Networks

EISCI
ISSN:0893-6080
被引量20
参考文献量43
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