Improved PLS regression based on SVM classification for rapid analysis of coal properties by near-infrared reflectance spectroscopy

Sensors and Actuators, B: Chemical

Volumn 193, Issue , 2014, Pages 723-729

  Wang, Yasheng ^a   Yang, Meng ^b   Wei, Gao ^c   Hu, Ruifen ^a   Luo, Zhiyuan ^b   Li, Guang ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b UNIVERSITY OF LONDON   (United Kingdom)

^c Fenxi Ming Group Ltd   (China)

Author keywords

Coal analysis; Near infrared reflectance spectra; Partial Least Square regression; Support Vector Machine

Indexed keywords

CLASSIFICATION ACCURACY; COAL ANALYSIS; COAL CLASSIFICATION; NEAR INFRA-RED REFLECTANCES; NEAR INFRARED REFLECTANCE SPECTROSCOPY; PARTIAL LEAST SQUARE REGRESSION; PREDICTIVE ABILITIES; PRINCIPLE COMPONENTS ANALYSIS;

COAL; COKING; INFRARED DEVICES; MOISTURE; PRINCIPAL COMPONENT ANALYSIS; REFLECTION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

COAL MINES;

EID: 84891720207     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2013.12.028     Document Type: Article

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