半马尔科夫跳变忆阻神经网络的同步采样控制研究
Sampled-Data Synchronization Control for Semi-Markovian Jump Memristive Neural Networks
摘要: 本文主要基于采样控制研究半马尔科夫跳变时滞忆阻神经网络的同步问题。首先，通过微分包含、集值映射以及鲁棒分析方法将具有半马尔科夫跳变的时滞忆阻神经网络转化为带不确定参数的传统半马尔科夫跳变神经网络。其次，基于采样控制器，构造一个包含采样信息更多的Lyapunov泛函，使得采样周期更长，从而获得保守性更低的同步判据。最后，通过实例仿真来验证所得理论结果的有效性。
Abstract: This paper investigates the problem of sampled-data synchronization for semi-Markovian jump de-layed memristive neural networks. First, the semi-Markovian jump delayed memristive neural networks are converted into traditional semi-Markovian jump neural network with uncertain pa-rameters by differential inclusion, set-valued mapping and robust analysis. Next, based on the sam-pled-data controller, a Lyapunov functional with more sampling information is constructed to make the sampling period longer, so as to obtain a new synchronization standard with lower conservatism. Finally, a numerical example illustrates the feasibility and effectiveness of the obtained theory re-sults.
文章引用：权沈爱, 熊良林. 半马尔科夫跳变忆阻神经网络的同步采样控制研究[J]. 应用数学进展, 2022, 11(7): 4917-4932. https://doi.org/10.12677/AAM.2022.117516

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