摘要
对于具有混合时变时滞的主从神经网络指数采样同步控制问题,运用Lyapunov-Krasovskii泛函方法以及线性矩阵不等式方法对其进行研究。通过构造新的增广Lyapunov-Krasovskii泛函,并对其导数采用一系列不等式方法进行界定,获得具有更小保守性的时滞相关指数同步判据。同时,基于最大采样间隔以及衰减率,得到可行控制器。最后,通过数值算例及仿真证明此方法的优越性以及可行性。
Sampled-data exponential synchronization problems for master-slave neural networks with time-varying mixed delays were investigated with the Lyapunov-Krasovskii functional approach and linear matrix inequality（LMI）.By constructing the novel Lyapunov-Krasovskii functions and estimating the derivative of them with a set of inequality methods, exponential synchronization criteria with time-varying delays were derived, which had less conservative. Then, depending upon the maximum sampling interval and decay rate, the desired sampled-data controller was achieved. The numerical example and simulation results verify the superiority and effectiveness of the approach.
作者
陈刚
王信
肖伸平
杜博文
王聪聪
罗昌胜
CHEN Gang1,2,WANG Xin1,2,XIAO Shenping1,2,DU Bowen1,2,WANG Congcong1,2,LUO Changsheng1,2(1. School of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou 412007,China; 2. Key Laboratory for Electric Drive Control and Intelligent of Hunan Province,Zhuzhou 412007,Chin)
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第6期1432-1439,共8页
Journal of Central South University:Science and Technology
基金
湖南省自然科学基金项目(2018JJ4075)
国家自然科学基金资助项目(61672225
61304064)~~
