Abstract
Linear and nonlinear quantitative structure–property relationship (QSPR) models for prediction of liquid thermal conductivity of 116 alcohols were developed from a set of 1199 molecular descriptors by using genetic function approximation (GFA) and adaptive neuro-fuzzy inference system (ANFIS). Highly statistically significant model was obtained by GFA method when the number of descriptors in the equation was set to 5. Results of the GFA model were further compared with nonlinear QSAR model generated by ANFIS. The results surprisingly showed more or less the same quality for GFA and ANFIS modeling, according to the squared correlation coefficient values for testing set, which were 0.9521 and 0.9595, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
