This paper researches the problem of p-norm fixed-time synchronization for a class of delayed inertial complex-valued neural networks (ICVNNs). By using reduced-order transformation and separating real and imaginary parts of complex-valued parameters, the second-order ICVNNs can be converted into the form of first-order real-valued differential equations. Then some new flexible and adjustable algebraic criteria to ensure the fixed-time synchronization of ICVNNs are established by means of the non-smooth Lyapunov function and inequality analytical techniques. Moreover, the settling time of fixed-time synchronization is theoretically estimated, which does not depend on the initial value of systems. Finally, simulation examples and applications are presented to illustrate the validity and availability of the obtained results.

本文研究了一类时滞惯性复值神经网络（ICVNN）的p范数固定时间同步问题。通过使用降阶转化和分离实部和虚的复数值的参数部分中，第二阶ICVNNs可被转换成的形式的一阶实值差分方程。然后通过非光滑的Lyapunov函数建立了一些新的灵活的可调节代数准则，以确保ICVNN的固定时间同步。和不平等分析技术。而且，理论上估计了固定时间同步的建立时间，它不依赖于系统的初始值。最后，通过仿真实例和应用实例说明了所得结果的有效性和有效性。