An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors
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 charact...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5353296 |
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| author | Huixin Tian Minwei Shuai Kun Li Xiao Peng |
| author_facet | Huixin Tian Minwei Shuai Kun Li Xiao Peng |
| author_sort | Huixin Tian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b1bd723f398e4f958cb5bb37be383fec |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b1bd723f398e4f958cb5bb37be383fec2025-02-03T01:09:01ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/53532965353296An Incremental Learning Ensemble Strategy for Industrial Process Soft SensorsHuixin Tian0Minwei Shuai1Kun Li2Xiao Peng3School of Electrical Engineering & Automation and Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin, ChinaSchool of Electrical Engineering & Automation and Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin, ChinaSchool of Economics and Management, Tianjin Polytechnic University, Tianjin, ChinaSchool of Electrical Engineering & Automation and Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin, ChinaWith 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.http://dx.doi.org/10.1155/2019/5353296 |
| spellingShingle | Huixin Tian Minwei Shuai Kun Li Xiao Peng An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors Complexity |
| title | An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors |
| title_full | An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors |
| title_fullStr | An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors |
| title_full_unstemmed | An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors |
| title_short | An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors |
| title_sort | incremental learning ensemble strategy for industrial process soft sensors |
| url | http://dx.doi.org/10.1155/2019/5353296 |
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