Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning

作     者：汤奇峰 李德伟 席裕庚 

作者机构：Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiaotong University 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2015年第20卷第2期

页      面：171-176页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 

基　　金：the National Natural Science Foundation of China(Nos.61374110 and 61074060) the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20120073110017) 

主　　题：soft-sensing semi-supervised learning(SSL) online correction neural network 

摘      要：Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.