p-th exponential synchronization of Cohen-Grossberg neural network with mixed time-varying delays and unknown parameters using impulsive control method
p-th exponential synchronization of Cohen-Grossberg neural network with mixed time-varying delays and unknown parameters using impulsive control method
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DOI：
10.1016/j.neucom.2016.09.002
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发表时间：
2016-12
期刊：
影响因子：
6
通讯作者：
Chaolong Zhang;F. Deng;Xueyan Zhao;Bo Zhang-
中科院分区：
文献类型：
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作者：
Chaolong Zhang;F. Deng;Xueyan Zhao;Bo Zhang-
本文研究了具有混合时变时滞和未知参数的Cohen-Grossberg神经网络的p阶指数同步问题。基于脉冲和不同时变时滞的神经网络模型，使得神经网络模型具有很强的通用性和实用性。利用李雅普诺夫稳定性理论和参数辨识方法，设计了一个非线性脉冲控制器，保证了响应系统与驱动系统的同步。我们的同步准则很容易验证，不需要解决任何线性矩阵不等式。这些结果推广了以前的一些已知结果，并消除了对神经网络的一些限制。最后通过数值算例和仿真验证了理论结果的有效性和优越性。
In this paper, we investigate thep-th exponential synchronization of Cohen–Grossberg neural network with mixed time-varying delays and unknown parameters by general impulsive controller. Based on impulsive and different time-varying delays, it makes the neural network model very general and practical. A nonlinear impulsive controller is designed to guarantee that the response system can be synchronized with a drive system by utilizing Lyapunov stability theory and parameter identification. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on the neural networks. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness and advantage of the theoretical results.