Department of Information Technology, Hunan Women's University, Changsha, Hunan 410002, PR China.
Department of Information Technology, Hunan Women's University, Changsha, Hunan 410002, PR China; College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China.
Neural Netw. 2015 Aug;68:96-110. doi: 10.1016/j.neunet.2015.04.011. Epub 2015 May 6.
This paper is concerned with the periodic synchronization problem for a general class of delayed neural networks (DNNs) with discontinuous neuron activation. One of the purposes is to analyze the problem of periodic orbits. To do so, we introduce new tools including inequality techniques and Kakutani's fixed point theorem of set-valued maps to derive the existence of periodic solution. Another purpose is to design a switching state-feedback control for realizing global exponential synchronization of the drive-response network system with periodic coefficients. Unlike the previous works on periodic synchronization of neural network, both the neuron activations and controllers in this paper are allowed to be discontinuous. Moreover, owing to the occurrence of delays in neuron signal, the neural network model is described by the functional differential equation. So we introduce extended Filippov-framework to deal with the basic issues of solutions for discontinuous DNNs. Finally, two examples and simulation experiments are given to illustrate the proposed method and main results which have an important instructional significance in the design of periodic synchronized DNNs circuits involving discontinuous or switching factors.
这篇论文研究了具有不连续神经元激活的广义时滞神经网络(DNN)的周期同步问题。目的之一是分析周期轨道问题。为此,我们引入了新的工具,包括不等式技术和 Kakutani 的集值映射不动点定理,以得出周期解的存在性。另一个目的是设计切换状态反馈控制,以实现具有周期系数的驱动-响应网络系统的全局指数同步。与以前关于神经网络周期同步的工作不同,本文中的神经元激活和控制器都允许不连续。此外,由于神经元信号中存在时滞,神经网络模型由泛函微分方程描述。因此,我们引入扩展的 Filippov 框架来处理不连续 DNN 的解的基本问题。最后,给出了两个实例和仿真实验,以说明所提出的方法和主要结果,这对涉及不连续或开关因素的周期性同步 DNN 电路的设计具有重要的指导意义。