Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays

Jin Hu, Jun Wang

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

165 Citations (Scopus)

Abstract

Memristor is a newly prototyped nonlinear circuit device. Its value is not unique and changes according to the value of the magnitude and polarity of the voltage applied to it. In this paper, a simplified mathematical model is proposed to characterize the pinched hysteretic feature of the memristor, a memristor-based recurrent neural network model is given, and its global stability is studied. Using differential inclusion, two sufficient conditions for the global uniform asymptotic stability of memristor-based recurrent neural networks are obtained.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/TerritorySpain
CityBarcelona
Period18/07/1023/07/10

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