P. He, Yangmin Li
{"title":"H∞ synchronization of coupled reaction-diffusion neural networks with mixed delays","authors":"P. He, Yangmin Li","doi":"10.1002/CPLX.21782","DOIUrl":null,"url":null,"abstract":"The reaction-diffusion neural network is often described by semilinear diffusion partial differential equation (PDE). This article focuses on the asymptotical synchronization and synchronization for coupled reaction-diffusion neural networks with mixed delays (that is, discrete and infinite distributed delays) and Dirichlet boundary condition. First, using the Lyapunov–Krasoviskii functional scheme, the sufficient condition is obtained for the asymptotical synchronization of coupled semilinear diffusion PDEs with mixed time-delays and this condition is represented by linear matrix inequalities (LMIs), which is easy to be solved. Then the robust synchronization is considered in temporal-spatial domain for the coupled semilinear diffusion PDEs with mixed delays and external disturbances. In terms of the technique of completing squares, the sufficient condition is obtained for the robust synchronization. Finally, a numerical example of coupled semilinear diffusion PDEs with mixed time-delays is given to illustrate the correctness of the obtained results. © 2016 Wiley Periodicals, Inc. Complexity, 2016","PeriodicalId":72654,"journal":{"name":"Complex psychiatry","volume":"88 1 1","pages":"42-53"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex psychiatry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/CPLX.21782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
混合时滞耦合反应-扩散神经网络的H∞同步
反应-扩散神经网络通常用半线性扩散偏微分方程来描述。本文主要研究了具有混合延迟(即离散和无限分布延迟)和Dirichlet边界条件的耦合反应-扩散神经网络的渐近同步和同步问题。首先,利用Lyapunov-Krasoviskii泛函格式,得到了具有混合时滞的耦合半线性扩散偏微分方程渐近同步的充分条件,该条件用线性矩阵不等式(lmi)表示,易于求解;然后考虑了具有混合时滞和外部干扰的耦合半线性扩散偏微分方程的时空鲁棒同步问题。利用平方补全技术,得到了鲁棒同步的充分条件。最后,给出了具有混合时滞的耦合半线性扩散偏微分方程的数值算例,验证了所得结果的正确性。©2016 Wiley期刊公司复杂性,2016
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