Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm
Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in com...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6457517 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547008127172608 |
|---|---|
| author | Wei Xie Jie-sheng Wang Cheng Xing Sha-Sha Guo Meng-wei Guo Ling-feng Zhu |
| author_facet | Wei Xie Jie-sheng Wang Cheng Xing Sha-Sha Guo Meng-wei Guo Ling-feng Zhu |
| author_sort | Wei Xie |
| collection | DOAJ |
| description | Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy. |
| format | Article |
| id | doaj-art-59410bfab91349c0b525bb8e673c36e9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-59410bfab91349c0b525bb8e673c36e92025-02-03T06:46:23ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/64575176457517Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization AlgorithmWei Xie0Jie-sheng Wang1Cheng Xing2Sha-Sha Guo3Meng-wei Guo4Ling-feng Zhu5School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, ChinaSoft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.http://dx.doi.org/10.1155/2020/6457517 |
| spellingShingle | Wei Xie Jie-sheng Wang Cheng Xing Sha-Sha Guo Meng-wei Guo Ling-feng Zhu Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm Complexity |
| title | Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm |
| title_full | Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm |
| title_fullStr | Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm |
| title_full_unstemmed | Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm |
| title_short | Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm |
| title_sort | adaptive hybrid soft sensor model of grinding process based on regularized extreme learning machine and least squares support vector machine optimized by golden sine harris hawk optimization algorithm |
| url | http://dx.doi.org/10.1155/2020/6457517 |
| work_keys_str_mv | AT weixie adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm AT jieshengwang adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm AT chengxing adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm AT shashaguo adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm AT mengweiguo adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm AT lingfengzhu adaptivehybridsoftsensormodelofgrindingprocessbasedonregularizedextremelearningmachineandleastsquaressupportvectormachineoptimizedbygoldensineharrishawkoptimizationalgorithm |