Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/157576
Title: Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
Authors: Xinsong Yang;Jinde Cao;Jinling Liang
Year: 2017
Publisher: IEEE
Abstract: This paper considers the global exponential synchronization of drive-response memristive neural networks (MNNs) with heterogeneous time-varying delays. Because the parameters of MNNs are state-dependent, the MNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, traditional robust control scheme cannot guarantee the synchronization of MNNs. Under the framework of Filippov solution, the drive and response MNNs are first transformed into systems with interval parameters. Then suitable controllers are designed to overcome the problem of mismatched parameters and synchronize the coupled MNNs. Based on some novel Lyapunov functionals and interval matrix inequalities, several sufficient conditions are derived to guarantee the exponential synchronization. Moreover, adaptive control is also investigated for the exponential synchronization. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.
URI: http://localhost/handle/Hannan/157576
volume: 28
issue: 8
More Information: 1878,
1888
Appears in Collections:2017

Files in This Item:
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Title: Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
Authors: Xinsong Yang;Jinde Cao;Jinling Liang
Year: 2017
Publisher: IEEE
Abstract: This paper considers the global exponential synchronization of drive-response memristive neural networks (MNNs) with heterogeneous time-varying delays. Because the parameters of MNNs are state-dependent, the MNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, traditional robust control scheme cannot guarantee the synchronization of MNNs. Under the framework of Filippov solution, the drive and response MNNs are first transformed into systems with interval parameters. Then suitable controllers are designed to overcome the problem of mismatched parameters and synchronize the coupled MNNs. Based on some novel Lyapunov functionals and interval matrix inequalities, several sufficient conditions are derived to guarantee the exponential synchronization. Moreover, adaptive control is also investigated for the exponential synchronization. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.
URI: http://localhost/handle/Hannan/157576
volume: 28
issue: 8
More Information: 1878,
1888
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7469328.pdf1.42 MBAdobe PDFThumbnail
Preview File
Title: Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
Authors: Xinsong Yang;Jinde Cao;Jinling Liang
Year: 2017
Publisher: IEEE
Abstract: This paper considers the global exponential synchronization of drive-response memristive neural networks (MNNs) with heterogeneous time-varying delays. Because the parameters of MNNs are state-dependent, the MNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, traditional robust control scheme cannot guarantee the synchronization of MNNs. Under the framework of Filippov solution, the drive and response MNNs are first transformed into systems with interval parameters. Then suitable controllers are designed to overcome the problem of mismatched parameters and synchronize the coupled MNNs. Based on some novel Lyapunov functionals and interval matrix inequalities, several sufficient conditions are derived to guarantee the exponential synchronization. Moreover, adaptive control is also investigated for the exponential synchronization. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.
URI: http://localhost/handle/Hannan/157576
volume: 28
issue: 8
More Information: 1878,
1888
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7469328.pdf1.42 MBAdobe PDFThumbnail
Preview File