Spectral kernel principal component selection based on empirical mode decomposition and genetic algorithm for modeling parameters of ball mill load

Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011

Volumn 1, Issue , 2011, Pages 932-935

  Tang, Jian ^a   Zhao, Lijie ^a,b   Yue, Heng ^a   Chai, Tianyou ^a   Yu, Wen ^c  

^a KEY LABORATORY OF INTEGRATED AUTOMATION OF PROCESS INDUSTRY   (China)

^b SHENYANG UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^c DEPARTAMENTO DE FÍSICA   (Mexico)

Author keywords

empirical mode decomposition (EMD); feature seleciton; genetic algorithms(GA); kernel principal component analysis (KPCA); mill load; partial least square(PLS)

Indexed keywords

EMPIRICAL MODE DECOMPOSITION; FEATURE SELECITON; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); MILL LOAD; PARTIAL LEAST SQUARE(PLS);

BALL MILLS; ENERGY UTILIZATION; FEATURE EXTRACTION; GENETIC ALGORITHMS; PARAMETER ESTIMATION; POWER SPECTRAL DENSITY; SENSORS; SIGNAL PROCESSING; SPECTRAL DENSITY;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79956046150     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICTA.2011.234     Document Type: Conference Paper

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