Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting
Proximal least squares support vector regression (PLSSVR) is a novel regression machine that combines the advantages of proximal support vector regression (PSVR) and least squares support vector regression (LSSVR). It possesses the traits of high efficiency, simplicity, and good generalization abili...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945315/ |
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| author | Ting Zhou Shiguang Zhang |
| author_facet | Ting Zhou Shiguang Zhang |
| author_sort | Ting Zhou |
| collection | DOAJ |
| description | Proximal least squares support vector regression (PLSSVR) is a novel regression machine that combines the advantages of proximal support vector regression (PSVR) and least squares support vector regression (LSSVR). It possesses the traits of high efficiency, simplicity, and good generalization ability. This article establishes a new regression model by utilizing the above model frameworks, called PLSSVR model of heteroscedastic Gauss-Laplacian with mixed-noise-characteristics (PLSSVR-GLMH). The least square method is introduced and the regularization terms <inline-formula> <tex-math notation="LaTeX">${(1/2)}\cdot b_{t} ^{2}$ </tex-math></inline-formula> are added in model PLSSVR-GLMH respectively. It converts inequality constraint problems into simpler equality constraint problems and employs the Augmented Lagrange multiplier method to solve the proposed model. This not only improves the training speed and generalization ability, but also effectively enhances the prediction accuracy. Because the wind-speed forecasting error approximately conforms to the Gauss-Laplacian mixture noise distribution, in the real-world wind-speed dataset, the prediction accuracy of the PLSSVR-GLM model was significantly improved compared to the PLSSVR and LSSVR-GLM models. The experimental results validate the efficacy and practicality of the proposed method. |
| format | Article |
| id | doaj-art-b07f0053ebf14089ba49a3a47f76a56f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b07f0053ebf14089ba49a3a47f76a56f2025-08-20T03:07:20ZengIEEEIEEE Access2169-35362025-01-0113569465695710.1109/ACCESS.2025.355558010945315Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed ForecastingTing Zhou0https://orcid.org/0000-0001-5155-8301Shiguang Zhang1https://orcid.org/0000-0001-5047-8481School of Information Engineering, Shandong Management University, Jinan, ChinaSchool of Information Engineering, Shandong Management University, Jinan, ChinaProximal least squares support vector regression (PLSSVR) is a novel regression machine that combines the advantages of proximal support vector regression (PSVR) and least squares support vector regression (LSSVR). It possesses the traits of high efficiency, simplicity, and good generalization ability. This article establishes a new regression model by utilizing the above model frameworks, called PLSSVR model of heteroscedastic Gauss-Laplacian with mixed-noise-characteristics (PLSSVR-GLMH). The least square method is introduced and the regularization terms <inline-formula> <tex-math notation="LaTeX">${(1/2)}\cdot b_{t} ^{2}$ </tex-math></inline-formula> are added in model PLSSVR-GLMH respectively. It converts inequality constraint problems into simpler equality constraint problems and employs the Augmented Lagrange multiplier method to solve the proposed model. This not only improves the training speed and generalization ability, but also effectively enhances the prediction accuracy. Because the wind-speed forecasting error approximately conforms to the Gauss-Laplacian mixture noise distribution, in the real-world wind-speed dataset, the prediction accuracy of the PLSSVR-GLM model was significantly improved compared to the PLSSVR and LSSVR-GLM models. The experimental results validate the efficacy and practicality of the proposed method.https://ieeexplore.ieee.org/document/10945315/Heteroscedastic gauss-laplacian noiseleast squares support vector regressionmixed-noise-characteristicsproximal modelwind-speed forecasting |
| spellingShingle | Ting Zhou Shiguang Zhang Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting IEEE Access Heteroscedastic gauss-laplacian noise least squares support vector regression mixed-noise-characteristics proximal model wind-speed forecasting |
| title | Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting |
| title_full | Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting |
| title_fullStr | Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting |
| title_full_unstemmed | Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting |
| title_short | Proximal <italic>LSSVR</italic> of Gauss-Laplacian With Mixed-Noise-Characteristics and Its Applications for Short-Term Wind-Speed Forecasting |
| title_sort | proximal italic lssvr italic of gauss laplacian with mixed noise characteristics and its applications for short term wind speed forecasting |
| topic | Heteroscedastic gauss-laplacian noise least squares support vector regression mixed-noise-characteristics proximal model wind-speed forecasting |
| url | https://ieeexplore.ieee.org/document/10945315/ |
| work_keys_str_mv | AT tingzhou proximalitaliclssvritalicofgausslaplacianwithmixednoisecharacteristicsanditsapplicationsforshorttermwindspeedforecasting AT shiguangzhang proximalitaliclssvritalicofgausslaplacianwithmixednoisecharacteristicsanditsapplicationsforshorttermwindspeedforecasting |