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Passivity of Reaction-Diffusion Neural Networks Via Sampled-Data Control

  • University of Jinan
  • Guangxi University
  • Tiangong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A sampled-data (SD) control is introduced for passivity of reaction-diffusion neural networks (RDNNs). With an appropriate Lyapunov functional (LF), a SD control design is developed based on linear matrix inequalities to guarantee the passivity of closed-loop RDNNs. Lastly, we provide a numerical example to support the presented method.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2160-2165
Number of pages6
ISBN (Electronic)9781728124858
DOIs
StatePublished - Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/TerritoryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • linear matrix inequalities
  • passivity
  • reaction-diffusion neural networks
  • sampled-data control

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