具有无界时变时滞的复值神经网络的全局同步。
Global synchronization of complex-valued neural networks with unbounded time-varying delays.
机构信息
School of Artificial Intelligence and 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.
出版信息
Neural Netw. 2023 May;162:309-317. doi: 10.1016/j.neunet.2023.02.041. Epub 2023 Mar 6.
This paper investigates global synchronization of complex-valued neural networks (CVNNs) with unbounded time-varying delays. By applying analytical method and inequality techniques, an algebraic criterion is established to ensure global synchronization of the CVNNs via a devised feedback controller, which generalizes some existing outcomes. Finally, two numerical simulations and one application in image encryption are provided to verify the effectiveness of the theoretical results.
本文研究了具有无界时变时滞的复值神经网络（CVNN）的全局同步。通过应用分析方法和不等式技术，建立了一个代数判据，通过设计的反馈控制器确保 CVNN 的全局同步，推广了一些已有结果。最后，通过两个数值仿真和一个图像加密应用验证了理论结果的有效性。
Zotero 插件