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时变时滞神经网络的时滞相关鲁棒稳定性和耗散性分析
引用本文:肖伸平,练红海,陈刚,冯磊. 时变时滞神经网络的时滞相关鲁棒稳定性和耗散性分析[J]. 控制与决策, 2017, 32(6): 1084-1090
作者姓名:肖伸平  练红海  陈刚  冯磊
作者单位:1. 湖南工业大学电气与信息工程学院,湖南株洲412007;2. 电传动控制与智能装备湖南省重点实验室,湖南株洲412007,1. 湖南工业大学电气与信息工程学院,湖南株洲412007;2. 电传动控制与智能装备湖南省重点实验室,湖南株洲412007,1. 湖南工业大学电气与信息工程学院,湖南株洲412007;2. 电传动控制与智能装备湖南省重点实验室,湖南株洲412007,1. 湖南工业大学电气与信息工程学院,湖南株洲412007;2. 电传动控制与智能装备湖南省重点实验室,湖南株洲412007
基金项目:国家自然科学基金项目(61672225,61304064);国家火炬计划项目(2015GH712901);湖南省自然科学基金项目(2015JJ3064,2015JJ5021);湖南省教育厅科研基金项目(15B067);广东省特种光纤材料与器件工程技术研究开发中心开放基金项目.
摘    要:研究时变时滞神经网络的鲁棒稳定性和耗散性问题.充分利用积分项的时滞信息和激励函数条件构造一个合适的增广LK泛函;利用自由矩阵积分不等式处理LK泛函的导数,得到一个低保守性的时滞相关稳定判据;将所获得的结论延伸至神经网络的耗散性分析,并推导出一个确保神经网络严格$(mathcalX, mathcalY,mathcalZ)-gamma$-耗散的充分条件.最后通过3个数值算例验证了所提出方法的可行性和优越性.

关 键 词:神经网络  稳定性  耗散性  自由矩阵积分不等式  时变时滞

Delay-dependent robust stability and dissipativity analysis of neural networks with time-varying delays
XIAO Shen-ping,LIAN Hong-hai,CHEN Gang and FENG Lei. Delay-dependent robust stability and dissipativity analysis of neural networks with time-varying delays[J]. Control and Decision, 2017, 32(6): 1084-1090
Authors:XIAO Shen-ping  LIAN Hong-hai  CHEN Gang  FENG Lei
Affiliation:1. School of Electrical and Information Engineering,Hu''nan University of Technology,Zhuzhou 412007,China;2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hu''nan Province,Zhuzhou 412007,China,1. School of Electrical and Information Engineering,Hu''nan University of Technology,Zhuzhou 412007,China;2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hu''nan Province,Zhuzhou 412007,China,1. School of Electrical and Information Engineering,Hu''nan University of Technology,Zhuzhou 412007,China;2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hu''nan Province,Zhuzhou 412007,China and 1. School of Electrical and Information Engineering,Hu''nan University of Technology,Zhuzhou 412007,China;2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hu''nan Province,Zhuzhou 412007,China
Abstract:The problems of robust delay-dependent stability and dissipativity for neural networks(NNs) with time-varying delay is investigated. A proper augmented Lyapunov-Krasovskii functional(LKF) is constructed, which fully utilizes the information of time-delay in integral term and the neuron activation function conditions. Then, by employing the free-matrix-based integral inequality to handle the derivative of the LKF, a less conservative delay-dependent stability criteria is obtained. The acquired conclusion is extended to the analysis of dissipativity for delayed NNs, and a suficient condition is derived to guarantee the NNs strictly$(mathcalX, mathcalY, mathcalZ)-gamma$-dissipative. The superiority and feasibility of presented approaches are verified via three numerical examples.
Keywords:
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