文献类型：期刊文献

英文题名：Fixed-time synchronization of delayed inertial neural networks with impulsive control via two pinning rules

作者：Chen, Tao[1]; Peng, Shiguo[2]; Fu, Zhiwen[3]; Huang, Ruitao[1]; Mai, Guizhen[4]

机构：[1] School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China; [2] School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; [3] School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China; [4] School of Computer Science [School of Artificial Intelligence], Guangzhou Maritime, Guangzhou, 510700, China

年份：2026

卷号：159

外文期刊名：Communications in Nonlinear Science and Numerical Simulation

收录：EI(收录号：20260920154827)

语种：英文

外文关键词：Control theory - Neural networks - Neurons - Stability criteria - Synchronization

外文摘要：This study addresses the fixed-time synchronization in delayed inertial neural networks through a hybrid control framework combining continuous fixed-time law that avoids chattering with the pinning impulsive controller. Unlike conventional settling time estimation approaches, the proposed impulse-dependent estimation method yields less conservative fixed-time settling time. Furthermore, a pinning control technique is employed, dynamically adjusting pinned neurons at each impulsive instant based on distinct criteria such as random selection or distance-based strategies. This can enhance control efficiency by dynamically controlling certain neurons. By integrating these rules with Lyapunov stability theory, we establish sufficient synchronization criteria for delayed inertial neural networks. Numerical simulations demonstrate that distance-based pinning strategies achieve superior performance compared to random selection methods, and visually corroborate that the settling time decreases as the number of impulses increases. ? 2026 Elsevier B.V.