Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy
According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A doub...
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Gespeichert in:
| Autor*in: |
ZHAO Kai [verfasserIn] LEI Meng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Gong-kuang zidonghua - Editorial Department of Industry and Mine Automation, 2021, 38(2012), 9, Seite 39-42 |
|---|---|
| Übergeordnetes Werk: |
volume:38 ; year:2012 ; number:9 ; pages:39-42 |
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DOAJ088498735 |
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| 520 | |a According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. | ||
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(DE-627)DOAJ088498735 (DE-599)DOAJ97463114e97643fcbf02e4b3ec656f11 DE-627 ger DE-627 rakwb chi TN1-997 ZHAO Kai verfasserin aut Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. coal quality analysis, coal ash, optimization of coal sample, ash prediction model, near infrared spectroscopy, spectrum feature of coal, double-level clustering method Mining engineering. Metallurgy LEI Meng verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 39-42 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:39-42 https://doaj.org/article/97463114e97643fcbf02e4b3ec656f11 kostenfrei http://www.gkzdh.cn/article/id/8617 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 39-42 |
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(DE-627)DOAJ088498735 (DE-599)DOAJ97463114e97643fcbf02e4b3ec656f11 DE-627 ger DE-627 rakwb chi TN1-997 ZHAO Kai verfasserin aut Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. coal quality analysis, coal ash, optimization of coal sample, ash prediction model, near infrared spectroscopy, spectrum feature of coal, double-level clustering method Mining engineering. Metallurgy LEI Meng verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 39-42 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:39-42 https://doaj.org/article/97463114e97643fcbf02e4b3ec656f11 kostenfrei http://www.gkzdh.cn/article/id/8617 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 39-42 |
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(DE-627)DOAJ088498735 (DE-599)DOAJ97463114e97643fcbf02e4b3ec656f11 DE-627 ger DE-627 rakwb chi TN1-997 ZHAO Kai verfasserin aut Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. coal quality analysis, coal ash, optimization of coal sample, ash prediction model, near infrared spectroscopy, spectrum feature of coal, double-level clustering method Mining engineering. Metallurgy LEI Meng verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 39-42 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:39-42 https://doaj.org/article/97463114e97643fcbf02e4b3ec656f11 kostenfrei http://www.gkzdh.cn/article/id/8617 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 39-42 |
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(DE-627)DOAJ088498735 (DE-599)DOAJ97463114e97643fcbf02e4b3ec656f11 DE-627 ger DE-627 rakwb chi TN1-997 ZHAO Kai verfasserin aut Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. coal quality analysis, coal ash, optimization of coal sample, ash prediction model, near infrared spectroscopy, spectrum feature of coal, double-level clustering method Mining engineering. Metallurgy LEI Meng verfasserin aut In Gong-kuang zidonghua Editorial Department of Industry and Mine Automation, 2021 38(2012), 9, Seite 39-42 (DE-627)1680984667 1671251X nnns volume:38 year:2012 number:9 pages:39-42 https://doaj.org/article/97463114e97643fcbf02e4b3ec656f11 kostenfrei http://www.gkzdh.cn/article/id/8617 kostenfrei https://doaj.org/toc/1671-251X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 38 2012 9 39-42 |
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Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy |
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According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. |
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According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. |
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According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ088498735</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230410113329.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230410s2012 xx |||||o 00| ||chi c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ088498735</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ97463114e97643fcbf02e4b3ec656f11</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TN1-997</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">ZHAO Kai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. 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