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Passivity analysis of Markov jump BAM neural networks with mode-dependent mixed time-delays via piecewise-constant transition rates

机译:依赖于模态混合时滞的马尔科夫跳跃BAM神经网络的无源分析

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摘要

Passivity problem is studied for Markov jump bi-directional associative memory (BAM) neural networks with both mode-dependent mixed time delays and time-varying transition rates. In this paper, we consider both discrete delay and distributed delay which are all switching based on Markov process r(t). Time varying transition rates are, respectively, discussed under the cases of known transition rates and partly unknown transition rates. The mode-dependent time-varying character of transition rates is supposed to be piecewise-constant. By utilizing LMIs technique and a class of Lyapunov functionals, a switching delay passivity criterion underlying known transition rates is derived, which can be easily checked by the Matlab LMI Tool Box. Furthermore, we extend the result to passivity analysis of Markov jump BAM neural networks with partly unknown transition rates. The results obtained relate on not only switching discrete delays but also switching distributed delays. Finally, a numerical example is given to illustrate the effectiveness of the results. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:研究了马尔可夫跳跃双向联想记忆(BAM)神经网络的无源问题,该神经网络具有与模式相关的混合时间延迟和随时间变化的过渡速率。在本文中,我们考虑离散延迟和分布式延迟,它们都是基于马尔可夫过程r(t)进行切换的。时变转换率分别在已知转换率和部分未知的转换率的情况下讨论。过渡速率的与模式有关的时变特性应该是分段恒定的。通过利用LMIs技术和一类Lyapunov功能,可以得出已知过渡速率基础的开关延迟无源标准,可以通过Matlab LMI工具箱轻松地对其进行检查。此外,我们将结果扩展到Markov跳跃BAM神经网络的无源分析,其转移速率部分未知。获得的结果不仅涉及切换离散延迟,而且涉及切换分布式延迟。最后,通过数值例子说明了结果的有效性。 (C)2016富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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    《Journal of the Franklin Institute》 |2016年第6期|1436-1459|共24页
  • 作者单位

    Baise Univ, Inst Math & Stat, Baise 533000, Guangxi, Peoples R China;

    Chongqing Univ Technol, Inst Math & Stat, Chongqing 400054, Peoples R China;

    Chongqing Univ Technol, Inst Math & Stat, Chongqing 400054, Peoples R China;

    S China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China;

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