Event-Triggered Prescribed Time Bipartite Synchronization of T-S Fuzzy Coopetition Neural Networks and Its Applications: A Parameters Optimizing Strategy
Zhen Wang, Xiangzheng Si, Huang Xia · IEEE Transactions on Industrial Informatics · 2025
This article is concerned with the issues of prescribed time bipartite synchronization (PTBS) for T-S fuzzy coopetition neural networks (CNNs) under a parameter optimization-based event-triggered control strategy and its application to image privacy protection. This study is motivated by three key considerations: first, the coexistence of competitive–cooperative interactions and T-S fuzzy rules makes the analysis of prescribed time synchronization more challenging; second, the goal of achieving PTBS while minimizing control costs necessitates the integration of control parameter optimization algorithms; and third, in industrial applications, ensuring the security of sensitive image data is of paramount importance. To address these challenges, an error system model is constructed by using the coordinate transformation method and IF–THEN fuzzy rules. Then, a fuzzy event-triggered switching controller without Zeno behavior is designed to trigger sampling for each fuzzy rule simultaneously. On this basis, a PTBS criterion for T-S fuzzy CNNs is established by using Lyapunov stability theory. To reduce control costs, a control parameter optimization algorithm based on the sparrow search algorithm is proposed for the first time. Finally, simulation results and applications in image privacy protection are provided, verifying the effectiveness and advantages of the control strategy in solving the PTBS problem.