Fixed-time synchronization of complex-valued memristive competitive neural networks based on two novel fixed-time stability theorems

The article delves into the fixed-time synchronization of complex-valued memristive competitive neural networks (CVMCNNs) based on two novel fixed-time stability theorems. It begins by discussing the historical context and significance of competitive neural networks (CNNs) and memristors, highlighting the advantages of complex-valued neural networks over real-valued ones. The research focuses on the practical applications of neural network synchronization in fields such as multi-agent cooperation and secure communication. The main contributions include a more general model for CVMCNNs, two new stability theorems that improve the estimation of synchronization time, and the design of controllers for achieving fixed-time synchronization in both 1-norm and 2-norm senses without separating real and imaginary parts. The article concludes with numerical examples that validate the theoretical results and demonstrate the superiority of the proposed methods over existing approaches.