中文摘要：

为提高对工况复杂的工业过程进行软测量建模的模型精度和泛化能力,提出了一种基于改进Bagging算法的高斯过程集成软测量建模方法。该算法采用高斯过程回归算法建立集成学习模型的基学习器,并在Bagging算法对训练样本重采样生成基学习器训练子集的基础上,采用基于正则化互信息的特征排序指标进行基学习器的输入特征抽取,实现有监督的特征扰动,从而改善学习器的差异度。待测样本进行软测量估计时,根据各高斯过程基学习器输出的方差自适应地选择基学习器进行集成输出。采用工业双酚A生产装置反应器的现场数据建模仿真,结果表明该方法是有效的。

英文摘要：

In order to improve the accuracy and generalization ability of soft-sensor for complex industrial process, a Gaussian process ensemble soft-sensor modeling algorithm based on the improved bagging algorithm is proposed. This algorithm uses Gaussian process regression algorithm to build base learners and the resample method of bagging algorithm to form training subsets of base learners. A criteria for feature ordering base on normalized mutual information is proposed with selecting input features of base learners, which can implement supervised feature perturbance in the ensemble modeling for the sake of improving the diversity between base learners. When estimating the output of the test sample according to the output variances given by Gaussian process base learners, several base learners are selected adaptively to calculate the output of ensemble model. A soft-sensor modeling simulation using the data from the reactors of industrial Bisphenol-A production units shows the effectiveness of the algorithm.