Abstract:

When soft sensor models were constructed for complicated chemical processes by traditional stochastic gradient Boosting (SGB), improper selection of shrinkage parameters would reduce convergence rate of the algorithm, engender overfitting, and sometimes make it difficult to obtain a satisfactory generalization performance. In order to solve this problem, a modified SGB ensemble learning soft sensor was proposed, in which Gaussian process regression (GPR) was adopted as base learner and shrinkage parameters were automatically adjusted according to feedback of a weak learner in each iteration such that both estimation accuracy and learning efficiency were improved. Simulation results in an industrial process of bisphenol A production showed that the modified integration algorithm had higher learning efficiency and generalization performance than traditional SGB models.

Key words: algorithm, computer simulation, integration, stochastic gradient Boosting, soft sensor

摘要：

在建立复杂化工过程软测量模型时，使用传统的随机梯度Boosting算法（SGB）建模若收缩参数v选取不当会明显降低算法收敛速度，且极易陷入过拟合，难以取得令人满意的泛化效果。为解决这一问题，提出了一种基于SGB集成学习的软测量建模方法，采用高斯过程回归作为基学习器，并针对SGB算法固有的不足，依据每一次迭代中弱学习机的反馈，自适应调整收缩参数v，改善了SGB算法的过度拟合，从而提高了集成模型的估计精度与学习效率。将该方法应用于某双酚A装置的软测量建模中，仿真结果表明，相比于传统SGB建模，该方法具有更高的泛化性能和学习效率。

关键词: 算法, 计算机模拟, 集成, 随机梯度Boosting, 软测量