Linear matrix equation (LME) is a kind of very important mathematical equation, and many practical problems in scientific and engineering fields can be described by LMEs in mathematics. In this paper, an improved finite time convergence zeroing neural network (FTCZNN) for online solving time-varying linear complex matrix equation (TVLCME) is realized. Different from the exponential convergence conventional zeroing neural network (CZNN), the new FTCZNN adopts a novel design formula for its error matrix converging to zero. Theoretical analysis and proof of the new FTCZNN converges to the theoretical solution of the TVLCME in finite time are provided. For comparison purpose, the CZNN is also developed for solving the same TVLCME. Compared with the exponentially converging CZNN, the new FTCZNN has great improvement in convergence performance, and the simulation results demonstrate that the new FTCZNN is a more effective and superior candidate for online solving TVLCME.

线性矩阵方程（LME）是一种非常重要的数学方程，LME在数学上可以描述科学和工程领域的许多实际问题。本文实现了一种用于在线求解时变线性复杂矩阵方程（TVLCME）的改进的有限时间收敛归零神经网络（FTCZNN）。与指数收敛传统的归零神经网络（CZNN）不同，新型FTCZNN的误差矩阵收敛到零，采用了新颖的设计公式。提供了新的FTCZNN在有限时间内收敛到TVLCME理论解的理论分析和证明。为了进行比较，还开发了CZNN以解决同一TVLCME。与指数收敛的CZNN相比，新的FTCZNN的收敛性能有了很大的提高，