Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/584888
Title: Lag Synchronization of Memristor-Based Coupled Neural Networks via omega Measure
Authors: Ning Li;Jinde Cao
subject: transmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback control
Year: 2016
Publisher: IEEE
Abstract: This 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.
URI: http://localhost/handle/Hannan/147542
http://localhost/handle/Hannan/584888
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
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7294682.pdf3.97 MBAdobe PDFThumbnail
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Title: Lag Synchronization of Memristor-Based Coupled Neural Networks via omega Measure
Authors: Ning Li;Jinde Cao
subject: transmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback control
Year: 2016
Publisher: IEEE
Abstract: This 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.
URI: http://localhost/handle/Hannan/147542
http://localhost/handle/Hannan/584888
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7294682.pdf3.97 MBAdobe PDFThumbnail
Preview File
Title: Lag Synchronization of Memristor-Based Coupled Neural Networks via omega Measure
Authors: Ning Li;Jinde Cao
subject: transmittal delay.|memristor-based coupled neural networks|lag synchronization|parameters mismatch|Feedback control
Year: 2016
Publisher: IEEE
Abstract: This 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.
URI: http://localhost/handle/Hannan/147542
http://localhost/handle/Hannan/584888
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

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