Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/584888
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dc.contributor.authorNing Lien_US
dc.contributor.authorJinde Caoen_US
dc.date.accessioned2020-05-20T08:33:35Z-
dc.date.available2020-05-20T08:33:35Z-
dc.date.issued2016en_US
dc.identifier.issn2162-237Xen_US
dc.identifier.issn2162-2388en_US
dc.identifier.other10.1109/TNNLS.2015.2480784en_US
dc.identifier.urihttp://localhost/handle/Hannan/147542en_US
dc.identifier.urihttp://localhost/handle/Hannan/584888-
dc.description.abstractThis paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7294682.pdfen_US
dc.subjecttransmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback controlen_US
dc.titleLag Synchronization of Memristor-Based Coupled Neural Networks via omega Measureen_US
dc.typeArticleen_US
dc.journal.volume27en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Neural Networks and Learning Systemsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorNing Lien_US
dc.contributor.authorJinde Caoen_US
dc.date.accessioned2020-05-20T08:33:35Z-
dc.date.available2020-05-20T08:33:35Z-
dc.date.issued2016en_US
dc.identifier.issn2162-237Xen_US
dc.identifier.issn2162-2388en_US
dc.identifier.other10.1109/TNNLS.2015.2480784en_US
dc.identifier.urihttp://localhost/handle/Hannan/147542en_US
dc.identifier.urihttp://localhost/handle/Hannan/584888-
dc.description.abstractThis paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7294682.pdfen_US
dc.subjecttransmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback controlen_US
dc.titleLag Synchronization of Memristor-Based Coupled Neural Networks via omega Measureen_US
dc.typeArticleen_US
dc.journal.volume27en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Neural Networks and Learning Systemsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7294682.pdf3.97 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNing Lien_US
dc.contributor.authorJinde Caoen_US
dc.date.accessioned2020-05-20T08:33:35Z-
dc.date.available2020-05-20T08:33:35Z-
dc.date.issued2016en_US
dc.identifier.issn2162-237Xen_US
dc.identifier.issn2162-2388en_US
dc.identifier.other10.1109/TNNLS.2015.2480784en_US
dc.identifier.urihttp://localhost/handle/Hannan/147542en_US
dc.identifier.urihttp://localhost/handle/Hannan/584888-
dc.description.abstractThis paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7294682.pdfen_US
dc.subjecttransmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback controlen_US
dc.titleLag Synchronization of Memristor-Based Coupled Neural Networks via omega Measureen_US
dc.typeArticleen_US
dc.journal.volume27en_US
dc.journal.issue3en_US
dc.journal.titleIEEE Transactions on Neural Networks and Learning Systemsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7294682.pdf3.97 MBAdobe PDFThumbnail
Preview File