Time-varying linear complementarity problem (TLCP) has received a great deal of attention due to its broad variety of scientific and engineering applications. Several efficient Zhang neural networks are introduced for solving TLCP in this paper. Theoretical analysis shows that the related error function of the model proposed in this paper eventually tends to zero. The state convergence time periods of those Zhang neural networks with three types of activation functions are proved to be finite and can be quantitatively estimated by using some given parameters. Further, it is shown that the proposed neural network is of noise-tolerance, which means the neural network is more appropriate for a wider application. Moreover, in order to implement neural network numerically, a related discrete-time version is also studied. Finally, numerical simulations confirm the analysis of the proposed models concretely.

时变线性互补问题（TLCP）由于其广泛的科学和工程应用而受到了广泛的关注。本文介绍了几种有效的张神经网络用于求解TLCP。理论分析表明，本文提出的模型的相关误差函数最终趋于零。具有三种激活函数的Zhang神经网络的状态收敛时间被证明是有限的，并且可以使用一些给定的参数进行定量估计。此外，表明所提出的神经网络具有噪声容忍性，这意味着该神经网络更适合于更广泛的应用。此外，为了数字地实现神经网络，还研究了相关的离散时间版本。最后，