时滞神经网络随机抽样控制的状态估计  

State Estimation for Delayed Neural Networks With Stochastic Sampled-Data Control

作　　者：曾德强[1] 吴开腾[1] 宋乾坤[2] 张瑞梅 钟守铭[3] ZENG Deqiang;WU Kaiteng;SONG Qiankun;ZHANG Ruimei;ZHONG Shouming(Data Recovery Key Laboratory of Sichuan Province,and Numerical Simulation Key Laboratory of Sichuan Province,Neijiang Normal University Neijiang,Sichuan 541100,P.R.China;Department of Mathematics,Chongqing Jiaotong University,Chongqing 400074,P.R.China;School of Mathematical Sciences,University of Electronic Science and Technology of China,Chengdu 611731,P.R.China;Department of Applied Mathematics,University of Waterloo,Waterloo,Ontario N2L3G1,Canada)

机构地区：[1]内江师范学院四川省高等学校数值仿真重点实验室四川省数据恢复重点实验室,四川内江641100 [2]重庆交通大学数学系 [3]电子科技大学数学科学学院,成都611731 [4]滑铁卢大学应用数学系

出　　处：《应用数学和力学》2018年第7期821-832,共12页Applied Mathematics and Mechanics

基　　金：国家自然科学基金(61773004);重庆市高校创新团队项目(CXTDX201601022)~~

摘　　要：研究了时滞神经网络随机抽样控制的状态估计问题.首先,给出了随机抽样区间和抽样输入时滞的统一概率结构.基于此结构,构造了一个包含新的锯齿结构项的Lyapunov泛函.然后,运用不等式放缩技术,得到了误差系统随机稳定的保守性更低的标准,并设计出了合适的状态估计器.最后,数值仿真算例验证了所得结果的优势和有效性.The problem of the state estimation for delayed neural networks with stochastic sampled-data control was studied. First,a unified probability framework involving the stochastic sampling interval and the sampling input delay was proposed. Second,based on this unified probability framework,a new LyapunovKrasovskii functional（ LKF） with some new terms was constructed. Third,with this LKF and some inequality technologies,a less conservative criterion was established,which can ensure the stochastic stability of the error system. The desired state estimator was designed. Finally,numerical simulation results show the effectiveness and advantages of the proposed method.

关 键 词：时滞神经网络 随机抽样 状态估计 LYAPUNOV泛函 

分 类 号：O175.13[理学—数学]