Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Adaptive Neuro-Fuzzy Inference System (ANFIS) and Radial Basis Function Neural Network (RBF NN) have been developed for prediction of solubility of various gases in polystyrene. Solubility of butane, isobutene, carbon dioxide, 1,1,1,2-tetrafluoroethane (HFC-134a), 1-chloro-1,1-difluoroethane (HCFC-1...

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Veröffentlicht in:Expert systems with applications 2010-04, Vol.37 (4), p.3070-3074
Hauptverfasser: Khajeh, Aboozar, Modarress, Hamid
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Sprache:eng
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Adaptive Neuro-Fuzzy Inference System (ANFIS)
Polystyrene
Radial Basis Function Neural Network (RBF NN)
Solubility
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Modarress, Hamid
description Adaptive Neuro-Fuzzy Inference System (ANFIS) and Radial Basis Function Neural Network (RBF NN) have been developed for prediction of solubility of various gases in polystyrene. Solubility of butane, isobutene, carbon dioxide, 1,1,1,2-tetrafluoroethane (HFC-134a), 1-chloro-1,1-difluoroethane (HCFC-142b), 1,1-difluoroethane (HFC-l52a) and nitrogen in polystyrene is modeled by ANFIS and RBF NN in a wide range of pressure and temperature with high accuracy. The results obtained in this work indicate that ANFIS and RBF NN are effective methods for prediction of solubility of gases in polystyrene and have better accuracy and simplicity compared with the classical methods.
doi_str_mv 10.1016/j.eswa.2009.09.023
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subjects Adaptive Neuro-Fuzzy Inference System (ANFIS)
Polystyrene
Radial Basis Function Neural Network (RBF NN)
Solubility
title Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network
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