Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor
Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/1/304 |
| _version_ | 1797625917926801408 |
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| author | Rui Yang Yongbao Liu Xing He Zhimeng Liu |
| author_facet | Rui Yang Yongbao Liu Xing He Zhimeng Liu |
| author_sort | Rui Yang |
| collection | DOAJ |
| description | Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units. |
| first_indexed | 2024-03-11T10:03:16Z |
| format | Article |
| id | doaj.art-c0b59c82ddac484ea454f546caedf873 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T10:03:16Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c0b59c82ddac484ea454f546caedf8732023-11-16T15:17:22ZengMDPI AGEnergies1996-10732022-12-0116130410.3390/en16010304Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting FactorRui Yang0Yongbao Liu1Xing He2Zhimeng Liu3College of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaDue to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units.https://www.mdpi.com/1996-1073/16/1/304gas turbinemodel identificationmachine learningforgetting factor |
| spellingShingle | Rui Yang Yongbao Liu Xing He Zhimeng Liu Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor Energies gas turbine model identification machine learning forgetting factor |
| title | Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor |
| title_full | Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor |
| title_fullStr | Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor |
| title_full_unstemmed | Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor |
| title_short | Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor |
| title_sort | gas turbine model identification based on online sequential regularization extreme learning machine with a forgetting factor |
| topic | gas turbine model identification machine learning forgetting factor |
| url | https://www.mdpi.com/1996-1073/16/1/304 |
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