Abstract
| Language | English |
|---|---|
| Number of pages | 8 |
| Journal | IEEE Transactions on Control Systems Technology |
| DOIs | |
| Publication status | Published - 8 Oct 2018 |
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Keywords
- predictive models
- sensors
- support vector machines
- optimized production technology
- training
- data models
- multiple output
- relevant vector machine (RVM) ,
- soft sensors
- variable selection
- wastewater
Cite this
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Development of RVM-based multiple-output soft sensors with serial and parallel stacking strategies. / Liu, Yiqi; Liu, Bin; Zhao, Xiujie; Xie, Min.
In: IEEE Transactions on Control Systems Technology, 08.10.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development of RVM-based multiple-output soft sensors with serial and parallel stacking strategies
AU - Liu, Yiqi
AU - Liu, Bin
AU - Zhao, Xiujie
AU - Xie, Min
PY - 2018/10/8
Y1 - 2018/10/8
N2 - Soft sensors are the most commonly used tools to predict hard-to-measure variables in industrial processes. However, the presence of a large number of hard-to-measure variables always renders a generic single-output soft-sensor inadequate. This brief proposed two multiple-output soft sensors, the former based on a novel serial stacking relevant vector machine (RVM) models, called RVMS, by transforming multiple-output into single-output problems inspired by stacking generation and the latter based on ensemble multivariable RVM (EMRVM) models that are improved by ensemble learning. To further strengthen the predicted ability, least absolute shrinkage and selection operator and canonical correlation analysis are used to remove irrelevant and redundant information from raw features under the supervision of multivariate targets for RVMS and EMRVM, respectively. The proposed methodologies were first accessed by a well-established wastewater plant (WWTP) validation platform, Benchmark Simulation Model No.1 then evaluated by a real WWTP with data being collected from the field. The results demonstrated that the proposed strategies were able to significantly improve the prediction performance.
AB - Soft sensors are the most commonly used tools to predict hard-to-measure variables in industrial processes. However, the presence of a large number of hard-to-measure variables always renders a generic single-output soft-sensor inadequate. This brief proposed two multiple-output soft sensors, the former based on a novel serial stacking relevant vector machine (RVM) models, called RVMS, by transforming multiple-output into single-output problems inspired by stacking generation and the latter based on ensemble multivariable RVM (EMRVM) models that are improved by ensemble learning. To further strengthen the predicted ability, least absolute shrinkage and selection operator and canonical correlation analysis are used to remove irrelevant and redundant information from raw features under the supervision of multivariate targets for RVMS and EMRVM, respectively. The proposed methodologies were first accessed by a well-established wastewater plant (WWTP) validation platform, Benchmark Simulation Model No.1 then evaluated by a real WWTP with data being collected from the field. The results demonstrated that the proposed strategies were able to significantly improve the prediction performance.
KW - predictive models
KW - sensors
KW - support vector machines
KW - optimized production technology
KW - training
KW - data models
KW - multiple output
KW - relevant vector machine (RVM) ,
KW - soft sensors
KW - variable selection
KW - wastewater
U2 - 10.1109/TCST.2018.2871934
DO - 10.1109/TCST.2018.2871934
M3 - Article
JO - IEEE Transactions on Control Systems Technology
T2 - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
SN - 1063-6536
ER -