Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression
Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, C. Z. [verfasserIn] Pei, J. F. [verfasserIn] Wen, Y. F. [verfasserIn] Zhu, X. J. [verfasserIn] Wang, G. L. [verfasserIn] Xiao, T. T. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of superconductivity - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 23(2010), 5 vom: 28. Jan., Seite 741-744 |
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| Übergeordnetes Werk: |
volume:23 ; year:2010 ; number:5 ; day:28 ; month:01 ; pages:741-744 |
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| DOI / URN: |
10.1007/s10948-010-0728-6 |
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| 520 | |a Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. | ||
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10.1007/s10948-010-0728-6 doi (DE-627)SPR014868571 (SPR)s10948-010-0728-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Cai, C. Z. verfasserin aut Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Pei, J. F. verfasserin aut Wen, Y. F. verfasserin aut Zhu, X. J. verfasserin aut Wang, G. L. verfasserin aut Xiao, T. T. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 23(2010), 5 vom: 28. Jan., Seite 741-744 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:23 year:2010 number:5 day:28 month:01 pages:741-744 https://dx.doi.org/10.1007/s10948-010-0728-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 23 2010 5 28 01 741-744 |
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10.1007/s10948-010-0728-6 doi (DE-627)SPR014868571 (SPR)s10948-010-0728-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Cai, C. Z. verfasserin aut Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Pei, J. F. verfasserin aut Wen, Y. F. verfasserin aut Zhu, X. J. verfasserin aut Wang, G. L. verfasserin aut Xiao, T. T. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 23(2010), 5 vom: 28. Jan., Seite 741-744 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:23 year:2010 number:5 day:28 month:01 pages:741-744 https://dx.doi.org/10.1007/s10948-010-0728-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 23 2010 5 28 01 741-744 |
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10.1007/s10948-010-0728-6 doi (DE-627)SPR014868571 (SPR)s10948-010-0728-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Cai, C. Z. verfasserin aut Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Pei, J. F. verfasserin aut Wen, Y. F. verfasserin aut Zhu, X. J. verfasserin aut Wang, G. L. verfasserin aut Xiao, T. T. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 23(2010), 5 vom: 28. Jan., Seite 741-744 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:23 year:2010 number:5 day:28 month:01 pages:741-744 https://dx.doi.org/10.1007/s10948-010-0728-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 23 2010 5 28 01 741-744 |
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10.1007/s10948-010-0728-6 doi (DE-627)SPR014868571 (SPR)s10948-010-0728-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Cai, C. Z. verfasserin aut Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Pei, J. F. verfasserin aut Wen, Y. F. verfasserin aut Zhu, X. J. verfasserin aut Wang, G. L. verfasserin aut Xiao, T. T. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 23(2010), 5 vom: 28. Jan., Seite 741-744 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:23 year:2010 number:5 day:28 month:01 pages:741-744 https://dx.doi.org/10.1007/s10948-010-0728-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 23 2010 5 28 01 741-744 |
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10.1007/s10948-010-0728-6 doi (DE-627)SPR014868571 (SPR)s10948-010-0728-6-e DE-627 ger DE-627 rakwb eng 530 ASE 33.74 bkl Cai, C. Z. verfasserin aut Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Pei, J. F. verfasserin aut Wen, Y. F. verfasserin aut Zhu, X. J. verfasserin aut Wang, G. L. verfasserin aut Xiao, T. T. verfasserin aut Enthalten in Journal of superconductivity Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 23(2010), 5 vom: 28. Jan., Seite 741-744 (DE-627)313651175 (DE-600)2000540-4 1572-9605 nnns volume:23 year:2010 number:5 day:28 month:01 pages:741-744 https://dx.doi.org/10.1007/s10948-010-0728-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 33.74 ASE AR 23 2010 5 28 01 741-744 |
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530 ASE 33.74 bkl Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression Saturation magnetic induction (dpeaa)DE-He213 Amorphous magnetic alloys (dpeaa)DE-He213 Support vector regression (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 |
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Saturation Magnetic Induction Prediction for Amorphous Magnetic Alloys by Using Support Vector Regression |
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Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. |
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Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. |
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Abstract This paper illustrates an application of support vector regression (SVR) approach in forecasting the saturation magnetic induction (Bs) of amorphous magnetic alloys. SVR was trained and tested with an experimental data set comprised of five input variables, comprising the average number of valence electrons of amorphous magnetic alloys, mixed entropy, ratio of radii, difference of electron density, and difference of work function. The prediction performance of SVR was compared with that of artificial neural networks’ (ANN) model. The results demonstrate that the prediction ability of SVR is superior to that of ANN. This investigation indicates that SVR-based modeling is a practically useful tool in prediction of the saturation magnetic induction of amorphous alloys. This study provides a novel methodology to foresee the saturation magnetic induction in sintering/development of novel amorphous magnetic alloys possessing high saturation magnetic induction. |
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