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Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method
Author(s) -
Li Mengshan,
Huang Xingyuan,
Liu Hesheng,
Liu Bingxiang,
Wu Yan,
Deng Xiaozhen
Publication year - 2013
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.39059
Subject(s) - particle swarm optimization , solubility , artificial neural network , algorithm , polymer , polypropylene , computer science , fuzzy logic , cluster analysis , correlation coefficient , materials science , standard deviation , biological system , artificial intelligence , mathematics , chemistry , machine learning , composite material , statistics , biology
A four‐layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO‐FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO‐FNN model has been employed to investigate solubility of CO 2 in polystyrene, N 2 in polystyrene, and CO 2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO‐FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient ( R 2 ) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO‐FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013