A semi-supervised soft sensor modeling method based on the Tri-training GPR
Ensemble learning often achieves significantly superior generalization capabilities than a single learner by building and combining multiple learners.However,it is still a challenge to build a high-performance ensemble learning soft sensor model when the proportion of labeled data is small.In order to solve this problem,this paper proposes a soft-sensor modeling method based on the semi-supervised ensemble learning:Tri-training Gaussian process regression(Tri-training GPR)model.The modeling strategy gives full play to the advantages of semi-supervised learning,reducing the demand for labeled sample data in the modeling process.Under low data labeling rate,the labeled sample data set for modeling can still be expanded by filtering unlabeled data.Furthermore,a new idea of selecting high-confidence samples is proposed by combining the advantages of semi-supervised learning and ensemble learning.The proposed method was applied to the penicillin fermentation and debutanization tower process,and the soft sensor models for predicting penicillin and butane concentrations were established.Compared with the traditional modeling methods,the proposed method obtained better prediction results,which verified the effectiveness of the model.
soft senorensemble learningsemi-supervised learningTri-trainingGaussian process regressionprocess controlkinetic modelingchemical processes
万方数据
维普
