Han Zhen, Chen Naipeng, Wei Xiaofeng, Yuan Manman, Li Huijia
School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710072, China.
International School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Entropy (Basel). 2023 Aug 21;25(8):1241. doi: 10.3390/e25081241.
In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov-Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.
在本文中，作者分析了耦合忆阻神经网络（MNNs）之间投影同步的非线性效应及其应用。由于在参数失配和不同投影因子下完整信号传输困难，因此考虑了时变延迟和不确定性，以在非线性控制方法下实现具有多链路的MNNs的投影同步。通过扩展比较原理和一种处理失配参数的新方法，在不同类型的投影因子以及李雅普诺夫 - 克拉索夫斯基泛函（LKF）框架下确定了耦合MNNs投影收敛的充分准则。与传统的安全通信处理方法不同，驱动系统和响应系统之间同步误差的概念已被应用于解决信号加密/解密问题。最后，以数值形式进行的仿真通过图形展示，以证实理论结果的适用性。
