基于多样性加权相似度的集成局部加权偏最小二乘软测量建模  

Soft Sensor Development Based on Ensemble Locally Weighted Partial Least Squares Using Diverse Weighted Similarity Measures

作　　者：潘贝 金怀平 杨彪[1] 冯丽辉[1] 陈祥光 PAN Bei;JIN Huaiping;YANG Biao;FENG Lihui;CHEN Xiangguang(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;School of Chemistry and Chemical Engineering,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]北京理工大学化学与化工学院,北京100081

出　　处：《信息与控制》2019年第2期217-223,231,共8页Information and Control

基　　金：国家自然科学基金资助项目(61763020);云南省教育厅科学研究基金资助项目(2017ZZX149);云南省应用基础研究计划项目青年项目(2018FD040)

摘　　要：针对传统即时学习软测量方法仅考虑单一的相似度函数,难以有效处理复杂工业过程中的非线性特性,从而导致模型预测性能受限的问题,提出了一种基于多样性加权相似度(DWS)的集成局部加权偏最小二乘(LWPLS)软测量建模方法.首先采用随机子空间法和高斯混合聚类,构建一组多样性的训练样本子集;然后通过偏最小二乘回归分析确定输入特征权值,从而定义一组多样性加权相似度函数.在线实施阶段,对于任意的查询样本,基于多样性的相似度指标,可建立一组多样性的LWPLS软测量模型,随后引入集成学习策略实现难测变量的融合预测.在数值例子和脱丁烷塔过程中的应用结果表明了该方法的有效性.Conventional just-in-time(JIT)learning-based soft sensors only employ a single similarity measure and cannot efficiently deal with the nonlinear characteristics of complex industrial processes,resulting in poor prediction performance.To tackle this issue,we propose a soft sensor-modeling method based on ensemble locally weighted partial least squares(ELWPLS)using diverse weighted similarity measures(DWS).First,we create a set of diverse training subsets by repeatedly performing random subspace and Gaussian mixture model clustering.Then,we determine the weights of input variables using PLS regression,thereby allowing us to define a set of diverse weighted similarity measures.During the online implementation phase for an arbitrary query sample,a group of diverse LWPLS models can be built and further combined via ensemble learning to provide the final prediction.The effectiveness and superiority of the proposed DWS-ELWPLS soft sensor method is demonstrated through a numerical example and an industrial debutanizer column process.

关 键 词：软测量 即时学习 集成学习 加权相似度 局部加权偏最小二乘 

分 类 号：TQ021[化学工程]