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Modified recursive partial least squares algorithm with application to modeling parameters of ball mill load

机译：应用于球磨机载荷参数的修改递归部分最小二乘算法

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摘要

Recursive partial least squares (RPLS) regression is effectively used in process monitoring and modeling to deal with the stronger collinearity of the process variables and slow time-varying property of industrial processes. Aim at the RPLS cannot solve the modeling speed and the accuracy problems effectively, a modified sample-wise RPLS algorithm is proposed in this paper. It updates the PLS model according to the process status. We use the approximate linear dependence (ALD) condition to check each new sample. The model is reconstructed recursively such that the new samples satisfy the ALD condition. Experimental study on modeling parameters of ball mill load shows that the proposed modified RPLS algorithm is computationally faster, and the modeling accuracy is higher than conventional RPLS for the time-varying process.
机译：递归部分最小二乘（RPLS）回归有效地用于处理监测和建模，以处理过程变量的更强的共线性和工业过程的缓慢时变性。目的在RPLS无法有效地解决建模速度和精度问题，本文提出了一种改进的样本-WIES RPLS算法。它根据进程状态更新PLS模型。我们使用近似线性依赖（ALD）条件来检查每个新样本。递归地重建模型，使得新样本满足ALD条件。球磨机载荷建模参数的实验研究表明，所提出的修改RPLS算法正在计算得更快，并且建模精度高于传统RPLS的时变过程。

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