基于MLCRBMs-ELM的软测量建模

Soft Sensor Modeling Based on MLCRBMs-ELM

针对辅助变量之间相关性差挖掘困难等问题,提出一种基于特征提取集和极限学习机(MLCRBMsELML)的软测量建模方法.该方法首先通过聚类算法对输入数据集进行属性簇划分.按结果将数据集分类后输入至MLCRBMs特征提取集中进行同步的特征提取,提取到的特征子集经Blending层进行非线性融合得到新特征集,并输入至极限学习机(ELM)进行拟合得到最后的估计结果.实验结果表明该方法优于传统的方法,具有更高的预测精度和泛化性能.

Aiming at the difficult problem of poor correlation between auxiliary variables, this paper proposes a soft sensor modeling method based on feature extraction sets and extreme learning machine （MLCRBMs-ELML）. First of all, it divides the attribute of input into several classes by clustering algorithm. The classed data set enter to the MLCRBM~ feature extraction to perform synchronous feature extraction. Then, the extracted feature subsets are nonlinearly merged by the Blending layer and obtain the new feature set. Finally, the new feature set enter the ELM model to get the fitting results. The experimental results show that the soft sensor model is superior to the traditional method, and has higher prediction accuracy and generalization performance.