广义钉扎同步的具有不连续激活的时滞 Cohen-Grossberg 神经网络。
Generalized pinning synchronization of delayed Cohen-Grossberg neural networks with discontinuous activations.
机构信息
Fujian Province University Key Laboratory of Computation Science, School of Mathematical Sciences, Huaqiao University, 362021, Quanzhou, Fujian, PR China; School of Mathematics and Statistics, Central South University, 410083, Changsha, Hunan, PR China.
Changsha University of Science and Technology, 410014, Changsha, Hunan, PR China; College of Mathematics and Econometrics, Hunan University, 410082, Changsha, Hunan, PR China.
出版信息
Neural Netw. 2018 Aug;104:80-92. doi: 10.1016/j.neunet.2018.04.006. Epub 2018 Apr 21.
In this article, generalized pinning synchronization problem is investigated for a class of Cohen-Grossberg neural networks with discontinuous neuron activations and mixed delays. By designing generalized pinning state-feedback and adaptive controllers, several criteria for global exponential synchronization and global asymptotical synchronization of the drive-response based system are obtained in view of non-smooth analysis theory with generalized Lyapunov functional method, in which first pinning the neurons with very small self-inhibition and small amplification functions is pointed out. Some numerical examples are given to illustrate the feasibility of the obtained results.
本文研究了一类具有不连续神经元激活和混合时滞的 Cohen-Grossberg 神经网络的广义钉扎同步问题。通过设计广义钉扎状态反馈和自适应控制器，利用广义 Lyapunov 泛函方法和非光滑分析理论，针对基于驱动-响应的系统，给出了全局指数同步和全局渐近同步的几个准则，其中指出了首先对具有非常小的自抑制和小放大函数的神经元进行钉扎。给出了一些数值例子来说明所得到的结果的可行性。
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