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Volumn 489, Issue 2, 2003, Pages 125-136
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Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra
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Author keywords
Gas phase Fourier transform infrared spectra; Multi layer neural networks; Non linearity; Partial least squares regression
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Indexed keywords
FOURIER TRANSFORM INFRARED SPECTROSCOPY;
FUNCTIONS;
NONLINEAR SYSTEMS;
ORGANIC COMPOUNDS;
REGRESSION ANALYSIS;
BAND SHIFT;
GAUSSIAN BROADENING FUNCTIONS;
PARTIAL LEAST SQUARES REGRESSION;
NEURAL NETWORKS;
CHLORINE DERIVATIVE;
ABSORPTION;
ACCURACY;
ANALYTIC METHOD;
ARTICLE;
ARTIFICIAL NEURAL NETWORK;
CALIBRATION;
COMPARATIVE STUDY;
CONCENTRATION (PARAMETERS);
DATA ANALYSIS;
EXPERIMENT;
GAS PHASE FOURIER TRANSFORM INFRARED SPECTROSCOPY;
HEIGHT;
INFRARED SPECTROSCOPY;
NONLINEAR SYSTEM;
PERFORMANCE;
PRIORITY JOURNAL;
QUANTITATIVE ANALYSIS;
REGRESSION ANALYSIS;
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EID: 10644272571
PISSN: 00032670
EISSN: None
Source Type: Journal
DOI: 10.1016/S0003-2670(03)00726-8 Document Type: Article |
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Times cited : (82)
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References (17)
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