Two new NCP-functions are constructed firstly in this paper. The main purpose is to accelerate the process of solution-finding for tensor complementarity problem, which is implemented by neural network methods based on the promising NCP-functions. Moreover, the stability properties of the proposed neural networks are achieved via some theoretics and properties of generalization for linear and nonlinear complementarity problems. Plentiful numerical simulations demonstrate that the presented neural networks possess significantly better stability and comparable convergence rates than neural networks based on some existing NCP-functions.

中文翻译：

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基于新的NCP函数的神经网络方法解决张量互补问题

本文首先构造了两个新的NCP功能。主要目的是加快张量互补问题的求解过程，该过程是基于有前途的NCP函数通过神经网络方法实现的。此外，所提出的神经网络的稳定性是通过一些理论和线性和非线性互补问题的广义化特性来实现的。大量的数值模拟表明，与基于某些现有NCP函数的神经网络相比，所提出的神经网络具有明显更好的稳定性和可比的收敛速度。