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时滞惯性神经网络的全局指数稳定化和滞后同步控制。

Global exponential stabilization and lag synchronization control of inertial neural networks with time 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.

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. 2020 Jun;126:11-20. doi: 10.1016/j.neunet.2020.03.006. Epub 2020 Mar 7.

Abstract

The global exponential stabilization and lag synchronization control of delayed inertial neural networks (INNs) are investigated. By constructing nonnegative function and employing inequality techniques, several new results about exponential stabilization and exponential lag synchronization are derived via adaptive control. And the theoretical outcomes are developed directly from the INNs themselves without variable substitution. In addition, the synchronization results are also applied to image encryption and decryption. Finally, an example is presented to illustrate the validity of the derived results.

摘要

研究了时滞惯性神经网络(INN)的全局指数稳定性和滞后同步控制。通过构造非负函数并运用不等式技术,通过自适应控制得出了关于指数稳定性和指数滞后同步的几个新结果。并且这些理论结果是直接从 INN 本身推导出来的,而不需要变量替换。此外,同步结果也应用于图像加密和解密。最后,通过一个例子来说明所得到的结果的有效性。

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