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Volumn 945, Issue 1-2, 2002, Pages 173-184
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Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds
a
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Author keywords
Multiple linear regression analysis; Neural networks, artificial, self training; Quantitative structure activity relationships; Regression analysis; Retention times, relative
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Indexed keywords
CORRELATION METHODS;
ERRORS;
NEURAL NETWORKS;
ORGANIC COMPOUNDS;
POLARIZATION;
REGRESSION ANALYSIS;
DESCRIPTORS;
GAS CHROMATOGRAPHY;
ORGANIC COMPOUND;
ANALYTIC METHOD;
ANALYTICAL ERROR;
ARTICLE;
ARTIFICIAL NEURAL NETWORK;
CORRELATION COEFFICIENT;
GAS CHROMATOGRAPHY;
INTERMETHOD COMPARISON;
LINEAR REGRESSION ANALYSIS;
POLARIZATION;
PRIORITY JOURNAL;
QUANTITATIVE STRUCTURE ACTIVITY RELATION;
TECHNIQUE;
CHROMATOGRAPHY, GAS;
MODELS, CHEMICAL;
NEURAL NETWORKS (COMPUTER);
ORGANIC CHEMICALS;
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EID: 0036469866
PISSN: 00219673
EISSN: None
Source Type: Journal
DOI: 10.1016/S0021-9673(01)01513-8 Document Type: Article |
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Times cited : (27)
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References (22)
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