In this paper, to solve time-varying linear inequalities much faster, on basis of zeroing neural network (ZNN), two finite-time convergent ZNN (FTCZNN) models are proposed by exploiting two novel nonlinear activation functions (AFs). The first FTCZNN model is established by using the sign-bi-power AF which is termed as FTCZNN-S for presentation convenience. The second one is established by amending the sign-bi-power AF through adding a linear term, and called FTCZNN-SL. Compared with existing ZNN models for time-varying linear inequalities, the proposed two FTCZNN models possess prominent finite-time convergence performance. In addition, theoretical analysis is given to estimate the finite-time convergence upper bounds of those two FTCZNN models. Numerical comparative results ulteriorly validate the effectiveness and dominance of two FTCZNN models for finding the solution of time-varying linear inequalities.

本文为了更快地解决时变线性不等式，在归零神经网络（ZNN）的基础上，通过利用两个新颖的非线性激活函数（AF）提出了两个有限时间收敛的ZNN（FTCZNN）模型。第一个FTCZNN模型是通过使用符号双倍功率AF建立的，为了方便演示，它被称为FTCZNN-S。第二个是通过添加一个线性项来修改符号双幂AF来建立的，称为FTCZNN-SL。与现有的时变线性不等式的ZNN模型相比，所提出的两个FTCZNN模型具有突出的有限时间收敛性能。此外，进行了理论分析以估计这两个FTCZNN模型的有限时间收敛上限。