Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication

Abstract

This paper explores the realization of a predefined-time synchronization problem for coupled memristive neural networks with multi-links (MCMNN) via nonlinear control. Several effective conditions are obtained to achieve the predefined-time synchronization of MCMNN based on the controller and Lyapunov function. Moreover, the settling time can be tunable based on a parameter designed by the controller, which is more flexible than fixed-time synchronization. Then based on the predefined-time stability criterion and the tunable settling time, we propose a secure communication scheme. This scheme can determine security of communication in the aspect of encrypting the plaintext signal with the participation of multi-links topology and coupled form. Meanwhile, the plaintext signals can be recovered well according to the given new predefined-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the obtained theoretical results and the feasibility of the secure communication scheme.

Fixed/Predefined-time synchronization of memristor-based complex-valued BAM neural networks for image protection

“…With the support of technologies such as artificial intelligence, deep learning, and cloud computing, corresponding achievements have emerged in an endless stream (Su et al, 2021 ; Wen and Su, 2022 ), which also poses greater challenges to the large-scale information processing capacity of computing systems. Many researchers have explored the direction of brain-like research (Rubinov and Sporns, 2010 ; Zhao H. et al, 2021 ), trying to get inspiration from the structure of the human brain and the way of information processing. Associative memory is one of the most active behaviors in the human brain, and it simulates the ability of the real nervous system to process information.…”