时滞半Markov跳变神经网络系统事件驱动故障检测滤波器设计

Event-triggered fault detection filter design for semi-Markov jump neural networks with time delays

中文关键词  半Markov跳变神经网络  故障检测  事件驱动  时滞  Lyapunov-Krasovskii泛函

英文关键词  semi-Markov jump neural networks  fault detection  event-triggered: time delays  Lyapunov-Krasovskii functional

中文摘要

      本文针对一类具有时滞的半Markov跳变神经网络系统, 研究其基于滤波器的故障检测问题. 首先, 通过引 入一种事件驱动通讯机制和一个滤波器, 并将时变时滞以及网络诱导时滞考虑进来, 建立残差系统, 将故障检测问 题转化为求解满足一定H1性能指标的滤波问题. 然后, 基于Lyapunov-Krasovskii(L–K)泛函方法, 通过利用时滞乘 积型L–K泛函思想、积分不等式、改进逆凸矩阵不等式等方法, 以线性矩阵不等式形式给出故障检测滤波器的设计 方法. 最后, 数值仿真结果验证本文所设计故障检测滤波器的有效性与优越性.

英文摘要

      In this paper, the problem of fault detection is addressed for delayed semi-Markov jump neural networks (S-MNNs) based on an event-triggered communication scheme. By introducing a filter, the addressed fault detection problem is converted into an $H_\infty$ filtering problem. Then, based on the Lyapunov-Krasovskii functional theory, by constructing a delay-product-Lyapunov-Krasovskii functional and using the improved reciprocally convex combination approach, a fault detection filter that guarantees the asymptotic stability and the desired $H_\infty$ performance of the residual system is designed. Finally, numerical simulations are provided to illustrate the effectiveness and superiority of the presented results.