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Finite-time stabilization of memristor-based inertial neural networks with discontinuous activations and distributed delays

机译:具有不连续激活和分布时滞的基于忆阻器的惯性神经网络的有限时间稳定

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摘要

In this paper, the finite-time stabilization problem for memristor-based inertial neural networks (MINNs) with discontinuous activations (DAs) and distributed delays is investigated. To deal with the discontinuous property of the MINNs, the nonsmooth analysis theory is invoked. Furthermore, to simplify the MINNs with second-order state derivative, an order-reduced method is adopted. Then the second-order MINNs is transformed into a simpler first-order differential system. Moreover, the verifiable algebraic criteria are derived for the finite-time stabilization of MINNs with DAs and distributed delays under the designed control approach. Finally, the effectiveness of the obtained results are illustrated via numerical simulations. (c) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:本文研究了具有不连续激活(DAs)和分布时滞的基于忆阻器的惯性神经网络(MINN)的有限时间稳定问题。为了处理MINN的不连续性,调用了非平滑分析理论。此外,为了简化具有二阶状态导数的MINN,采用了降阶方法。然后将二阶MINN转换为更简单的一阶微分系统。此外,在设计的控制方法下,导出了具有DA和分布时滞的MINN的有限时间稳定化的可验证代数准则。最后,通过数值模拟说明了所得结果的有效性。 (c)2019富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2019年第6期|3628-3643|共16页
  • 作者单位

    China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China|Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China;

    China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China|Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Hubei, Peoples R China;

    China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Hubei, Peoples R China;

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