摘要
本文研究了具有加性时变时滞的神经网络系统混杂控制.基于混合触发机制构建了神经网络滤波误差系统模型,有效避免了芝诺现象通过求解一类矩阵不等式,给出了使得被控系统具有耗散滤波性能的充分条件,进而得到了基于混合触发机制的H_∞滤波、无源滤波、(Q,S,R)-耗散滤波和L2-L_∞滤波.最后,通过数值例子验证了方法的有效性.
This paper is concerned with the hybrid control for neural networks with additive time varying delays.The neural network filtering error system is modeled based on the hybrid triggered scheme,and the Zeno phenomenon can be effectively avoided.Some sufficient conditions are derived by solving the matrix inequalities for the existence of reliable dissipative filtering performance.Moreover,H∞filters,passive filters,(Q,S,R)-dissipative filters and L2-L∞filters are also obtained based on the hybrid trigger mechanism.Finally,some numerical examples are used to illustrate the effectiveness of the proposed methods.
作者
赵洋阳
张冬梅
王定江
ZHAO Yang-yang;ZHANG Dong-mei;WANG Ding-jiang(College of Science,Zhejiang University of Technology,Hangzhou Zhejiang 310023 China)
出处
《生物数学学报》
2020年第1期122-136,共15页
Journal of Biomathematics
基金
国家自然科学基金资助项目(61273016)
浙江自然科学基金资助项目(LY14F030010)
2017年度浙江工业大学研究生教改项目(2017202)
关键词
神经网络
混合触发机制
加性时变时滞
耗散理论
Neural Network
Hybrid Control
Additive Time Varying Delays
Dissipative Theory
作者简介
赵洋阳(1994-),女,浙江绍兴人,硕士研究生.E-mail:meidzh@zjut.edu.cn;通讯作者:张冬梅
