School of Automation, Huazhong University of Science and Technology, Wuhan, PR China.
School of Automation, Huazhong University of Science and Technology, Wuhan, PR China.
Neural Netw. 2019 Jan;109:159-167. doi: 10.1016/j.neunet.2018.10.004. Epub 2018 Oct 29.
This paper discusses the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs). By exploiting inequality techniques and by constructing appropriate Lyapunov functional, several sufficient conditions are obtained in the form of linear matrix inequalities (LMIs), which can be used to ascertain the passivity, output and input strict passivity of delayed RDMNNs. In addition, the passivity of RDMNNs without any delay is also considered. These conditions, represented by LMIs, can be easily verified by virtue of the Matlab toolbox. Finally, some illustrative examples are provided to substantiate the effectiveness and validity of the theoretical results, and to present an application of RDMNN in pseudo-random number generation.
本文讨论了时滞反应扩散忆阻神经网络（RDMNN）的被动性。通过利用不等式技术并构建适当的李雅普诺夫函数，以线性矩阵不等式（LMIs）的形式获得了几个充分条件，可用于确定时滞 RDMNN 的被动性、输出和输入严格被动性。此外，还考虑了没有任何时滞的 RDMNN 的被动性。这些条件以 LMIs 的形式表示，可以借助 Matlab 工具箱轻松验证。最后，提供了一些说明性示例，以证实理论结果的有效性和有效性，并展示 RDMNN 在伪随机数生成中的应用。
