This paper is dedicated to fixed-time anti-synchronization (FXTAS) and preassigned-time synchronization (PATS) of discontinuous fuzzy inertial neural networks with mixed time-varying delays. Different from the traditional continuous neural network model, the differential inclusion theory is utilized to deal with discontinuous systems. Then, based on Lyapunov stability theory, two operational and efficient pure power-law control schemes are designed to ensure FXTAS and PATS. PATS is more flexible than fixed-time synchronization because its settling-time can be set in advance according to the actual situation. Finally, two numerical simulations are given to confirm the consistency with the theoretical results obtained in this paper.

本文致力于具有混合时变延迟的不连续模糊惯性神经网络的固定时间反同步 (FXTAS) 和预分配时间同步 (PATS)。与传统的连续神经网络模型不同，微分包含理论用于处理不连续系统。然后，基于李雅普诺夫稳定性理论，设计了两种可操作且高效的纯幂律控制方案，以保证FXTAS和PATS。PATS比定时同步更灵活，因为它的建立时间可以根据实际情况提前设置。最后，给出了两个数值模拟，以证实与本文得到的理论结果的一致性。