Abstract The Zhang neural network (ZNN), a recurrent neural network, proposed in 2001, is particularly effective in solving time-varying problems. It has shown high efficiency and excellent performance in various applications. The weighted pseudoinverse is a useful tool in solving and analyzing the constrained least-squares problems. In this paper, we propose a ZNN model for computing the weighted pseudoinverse of a time-varying matrix. We show that our model converges globally and exponentially to the solution and our system is robust at the presence of small errors. A Matlab Simulink implementation of our model is presented. Our convergence analysis is verified by our experiments on testing matrices. A comparison study shows that our model has superior performance over the conventional gradient-based neural networks.

中文翻译：

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通过张神经网络计算时变 ML 加权伪逆

摘要 Zhang 神经网络 (ZNN) 是一种循环神经网络，于 2001 年提出，在解决时变问题方面特别有效。它在各种应用中都表现出高效率和卓越的性能。加权伪逆是求解和分析约束最小二乘问题的有用工具。在本文中，我们提出了一种用于计算时变矩阵的加权伪逆的 ZNN 模型。我们表明我们的模型全局地和指数地收敛到解决方案，并且我们的系统在存在小错误的情况下是稳健的。介绍了我们模型的 Matlab Simulink 实现。我们的收敛分析通过我们在测试矩阵上的实验得到了验证。比较研究表明，我们的模型比传统的基于梯度的神经网络具有更好的性能。