基于局部PLS的多输出过程自适应软测量建模方法(英文)

被引：8

作者：

邵伟明 ^{[1
]}

田学民 ^{[1
]}

王平 ^{[1
,2
]}

机构：

[1] College of Information and Control Engineering, China University of Petroleum (Huadong)

[2] State Key Laboratory of Heavy Oil Processing, China University of Petroleum

来源：

ChineseJournalofChemicalEngineering | 2014年 / 22卷 / 07期

关键词：

Local learning Online soft sensing Partial least squares F-test Multi-output process Process state division;

D O I：

暂无

中图分类号：

TP274 [数据处理、数据处理系统];

学科分类号：

0804 ; 080401 ; 080402 ; 081002 ; 0835 ;

摘要：

Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

引用

页码：828 / 836+843 +843

页数：10