Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide

Journal of Molecular Graphics and Modelling

Volumn 25, Issue 1, 2006, Pages 46-54

  Tabaraki, R ^a   Khayamian, T ^a   Ensafi, A A ^a  

^a ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Anthraquinone dyes; Supercritical carbon dioxide; WNN

Indexed keywords

ANTHRAQUINONE DYES; SUPERCRITICAL CARBON DIOXIDE; WAVELET NEURAL NETWORK (WNN) MODEL;

CARBON DIOXIDE; DYES; ERROR ANALYSIS; KETONES; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

NEURAL NETWORKS;

ANTHRAQUINONE DERIVATIVE; CARBON DIOXIDE; DYE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; COMPUTER PROGRAM; MULTIPLE LINEAR REGRESSION ANALYSIS; PREDICTION; PRESSURE; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE PROPERTY RELATION; SOLUBILITY; SUPERCRITICAL FLUID; TEMPERATURE SENSITIVITY; THEORETICAL MODEL; VALIDATION PROCESS;

ANTHRAQUINONES; CARBON DIOXIDE; COLORING AGENTS; MODELS, CHEMICAL; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PRESSURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY; TEMPERATURE;

EID: 33748128041     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2005.10.012     Document Type: Article

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