Comparing radial basis function and feed-forward neural networks assisted by linear discriminant or principal component analysis for simultaneous spectrophotometric quantification of mercury and copper

Analytica Chimica Acta

Volumn 537, Issue 1-2, 2005, Pages 331-338

  Akhlaghi, Y ^a   Kompany Zareh, M ^a  

^a INSTITUTE FOR ADVANCED STUDIES IN BASIC SCIENCES IASBS   (Iran)

Author keywords

Artificial neural networks; Linear discriminant analysis; Metal ions; PCA; Spectrophotometry

Indexed keywords

CALIBRATION; COPPER; FEEDFORWARD NEURAL NETWORKS; LINEAR SYSTEMS; MATHEMATICAL MODELS; MERCURY (METAL); PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; SPECTROPHOTOMETRY;

CALIBRATION MODELS; CHROMOGENIC REAGENT; DATA SETS; LINEAR DISCRIMINANT ANALYSIS (LDA);

METAL ANALYSIS;

COPPER; MERCURY;

ABSORPTION SPECTROSCOPY; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DISCRIMINANT ANALYSIS; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RANDOMIZATION; SPECTROPHOTOMETRY;

EID: 17044404602     PISSN: 00032670     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aca.2004.12.079     Document Type: Article

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