Dioxin Emission Concentration Prediction Using the Selective Ensemble Algorithm Based on Bayesian Inference and Binary Tree
Author:
Affiliation:
1. Faculty of Information Technology, Beijing University of Technology, Beijing, China
2. Departamento de Control Automatico, CINVESTAV-IPN, National Polytechnic Institute, Mexico City, Mexico
Funder
National Natural Science Foundation of China
Beijing Natural Science Foundation
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10258377.pdf?arnumber=10258377
Reference42 articles.
1. Predicting pipeline leakage in petrochemical system through GAN and LSTM
2. Soft sensing of dioxin emission concentration in solid waste incineration process based on multi-layer feature selection;qiao;Inf Control,2021
3. Semi-supervised soft sensor modeling based on two-subspace co-training algorithm;luo;CIESC Journal,2022
4. Bayesian Adaptive Inference and Adaptive Training
5. Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm;tang;Control Theory Appl,2021
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