Abstract This paper aims to investigate the fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks in the presence of parameter uncertainties. By using a new variable transformation and differential inclusions theory, we first establish two kinds of drive-response differential inclusion systems. By designing some novel discontinuous control inputs and using Lyapunov-Krasovskii functional approach, some sufficient criteria are derived for achieving fixed-time synchronization, and the corresponding setting times are estimated. The established results provide a new framework to deal with the inertial neural networks with fuzzy logics and discontinuous activation functions. Some previous works in the literature are extended and complement. Finally, two topical simulation examples are given to show the effectiveness of the developed main control schemes.

中文翻译：

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具有参数不确定性的不连续模糊惯性神经网络的定时同步分析

摘要 本文旨在研究存在参数不确定性的不连续模糊惯性神经网络的固定时间同步分析。利用新的变量变换和微分包含理论，我们首先建立了两种驱动-响应微分包含系统。通过设计一些新颖的不连续控制输入并使用Lyapunov-Krasovskii泛函方法，推导出了一些实现固定时间同步的充分准则，并估计了相应的设置时间。所建立的结果为处理具有模糊逻辑和不连续激活函数的惯性神经网络提供了一个新的框架。对以往文献中的一些著作进行了扩展和补充。最后，