A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks

This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from...

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Published inJournal of the Franklin Institute Vol. 355; no. 17; pp. 8857 - 8873
Main Authors Xiao, Shen-Ping, Lian, Hong-Hai, Teo, Kok Lay, Zeng, Hong-Bing, Zhang, Xiao-Hu
Format Journal Article
LanguageEnglish
Published Elmsford Elsevier Ltd 01.11.2018
Elsevier Science Ltd
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Abstract This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from r(t−τ¯) to r(tk−τ¯) and from r(t−τ¯) to r(tk+1−τ¯). Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature.
AbstractList This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from r(t−τ¯) to r(tk−τ¯) and from r(t−τ¯) to r(tk+1−τ¯). Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature.
This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from ... to ... and from ... to ... . Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature. (ProQuest: ... denotes formulae omitted.)
Author Zhang, Xiao-Hu
Lian, Hong-Hai
Xiao, Shen-Ping
Zeng, Hong-Bing
Teo, Kok Lay
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Snippet This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called...
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SubjectTerms Control systems design
Control theory
Finite element analysis
Linear matrix inequalities
Matrix methods
Neural networks
Parallel processing
Sampling
Synchronism
Title A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks
URI https://dx.doi.org/10.1016/j.jfranklin.2018.09.022
https://www.proquest.com/docview/2150519180
Volume 355
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