Department of Mathematics, Chongqing Normal University, Chongqing, 401331 China.
Department of Mathematics and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 China ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jidda, 21589 Saudi Arabia.
Cogn Neurodyn. 2014 Jun;8(3):239-49. doi: 10.1007/s11571-013-9277-6. Epub 2014 Jan 4.
This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen-Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen-Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.
本文研究了一类带有时变离散时滞和无界分布时滞的基于忆阻器的 Cohen-Grossberg 神经网络的全局指数同步问题。讨论了驱动-响应集。设计了一种新颖的控制器，使得响应（从）系统可以被控制以与驱动（主）系统同步。通过非线性变换，我们从所考虑的基于忆阻器的 Cohen-Grossberg 神经网络中得到一个替代系统。通过研究替代系统的全局指数同步，我们得到了所考虑的基于忆阻器的 Cohen-Grossberg 神经网络的相应同步准则。此外，本文所建立的条件易于验证，并改进了大多数关于基于忆阻器的神经网络稳定性和同步的现有文献中的条件。数值模拟结果表明了理论结果的有效性。
