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An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors
Joint Authors
Li, Kun
Li, Kun
Tian, Huixin
Shuai, Minwei
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases.
The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms.
According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data.
The ILES aggregates multiple sublearning machines by different weights for better accuracy.
When new data set arrives, a new submachine will be trained and aggregated into ensemble soft sensor model according to its weight.
The other submachines' weights will be updated at the same time.
Then a new updated soft sensor ensemble model can be obtained.
The weight updating rules are designed by considering the prediction accuracy of submachines with new arrived data.
So the update can fit the data change and obtain new information efficiently.
The sizing percentage soft sensor model is established to learn the information from the production data in the sizing of industrial processes and to test the performance of ILES, where the ELM (Extreme Learning Machine) is selected as the sublearning machine.
The comparison is done among new method, single ELM, AdaBoost.R ELM, and OS-ELM, and the test of the extensions is done with three test functions.
The results of the experiments demonstrate that the soft sensor model based on the ILES has the best accuracy and ability of online updating.
American Psychological Association (APA)
Tian, Huixin& Shuai, Minwei& Li, Kun& Li, Kun. 2019. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
Modern Language Association (MLA)
Tian, Huixin…[et al.]. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
American Medical Association (AMA)
Tian, Huixin& Shuai, Minwei& Li, Kun& Li, Kun. An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132086
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132086