Improving the System Identification of Transonic Wind Tunnel by a Regression Ensemble-Based Outlier Mining Method-Reference-Cited by-同舟云学术

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Improving the System Identification of Transonic Wind Tunnel by a Regression Ensemble-Based Outlier Mining Method

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 25th International Conference on Computational Science and Engineering (CSE)
language:
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Author:

Zhao Hongyan¹,Yu Dong²,Wang Biao³

Affiliation:

1. School of Computer Science and Engineering Northeastern University,Shenyang,China

2. National Engineering Research Center of High-End CNC Shenyang Institute of Computing Technology, Chinese Academy and Sciences,Shenyang,China

3. Shenyang Aerospace University,School of Automation,Shenyang,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10062089/10062495/10062545.pdf?arnumber=10062545

Reference14 articles.

1. A Bayesian Mixture Model Approach to Anomaly Detection with Application to Wind Tunnel Experiments

2. Detecting outliers in industrial systems using a hybrid ensemble scheme

3. Outlier detection based on a dynamic ensemble model: Applied to process monitoring

4. Soft clustering using weighted one-class support vector machines

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