Improved passivity analysis for neural networks with Markovian jumping parameters and interval time-varying delays
Improved passivity analysis for neural networks with Markovian jumping parameters and interval time-varying delays
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DOI：
10.1016/j.neucom.2014.12.023
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发表时间：
2015-05
期刊：
影响因子：
6
通讯作者：
Guoliang Chen;Jianwei Xia;Guangming Zhuang
中科院分区：
文献类型：
--
作者：
Guoliang Chen;Jianwei Xia;Guangming Zhuang
研究了具有马尔可夫跳变参数和区间时变时滞的神经网络的无源性分析问题。基于完全时滞分解思想构造了一种新的Lyapunov-Krasovskii泛函，并利用反凸技术，以线性矩阵不等式的形式建立了一些改进的时滞相关无源性准则。数值算例表明了所提方法的有效性。
The problem of passivity analysis for neural networks with Markovian jumping parameters and interval time-varying delays is investigated in this paper. By constructing a novel Lyapunov–Krasovskii functional based on the complete delay-decomposing idea and using reciprocally convex technique, some improved delay-dependent passivity criteria are established in terms of linear matrix inequalities. Numerical examples are also given to show the effectiveness of the proposed methods.