Shen Wenwen, Zeng Zhigang, Wang Leimin
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
Neural Netw. 2016 Nov;83:32-41. doi: 10.1016/j.neunet.2016.07.008. Epub 2016 Aug 8.
In this paper, stability for a class of uncertain switched neural networks with time-varying delay is investigated. By exploring the mode-dependent properties of each subsystem, all the subsystems are categorized into stable and unstable ones. Based on Lyapunov-like function method and average dwell time technique, some delay-dependent sufficient conditions are derived to guarantee the exponential stability of considered uncertain switched neural networks. Compared with general results, our proposed approach distinguishes the stable and unstable subsystems rather than viewing all subsystems as being stable, thus getting less conservative criteria. Finally, two numerical examples are provided to show the validity and the advantages of the obtained results.
本文研究了一类具有时变时滞的不确定切换神经网络的稳定性。通过探究每个子系统的依赖模式特性，将所有子系统分为稳定子系统和不稳定子系统。基于类Lyapunov函数方法和平均驻留时间技术，推导了一些依赖时滞的充分条件，以保证所考虑的不确定切换神经网络的指数稳定性。与一般结果相比，本文提出的方法区分了稳定子系统和不稳定子系统，而不是将所有子系统都视为稳定的，从而得到了保守性较低的准则。最后，给出了两个数值例子来说明所得结果的有效性和优势。
