| 时变时滞神经网络的时滞相关鲁棒稳定性和耗散性分析 |
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| 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. |
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| 作者姓名： | 肖伸平 练红海 陈刚 冯磊 |
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| 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 |
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| 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. |
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| 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.
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| 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 |
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| 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. |
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| Authors: | XIAO Shen-ping LIAN Hong-hai CHEN Gang and FENG Lei |
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| 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 |
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| 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. |
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