A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings

In our present work, a multi-layered feed-forward neural network (FFNN) model was designed and developed to predict electrical conductivity of multi-walled carbon nanotube (MWCNT) doped polystyrene (PS) latex nanocomposite (PS/MWCNT) film coatings using data set gathered from several conductivity me...

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Veröffentlicht in:	Applied soft computing Jg. 96; S. 106632
Hauptverfasser:	Demirbay, Barış, Kara, Duygu Bayram, Uğur, Şaziye
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2020
Schlagworte:	Artificial neural network Bayesian regulation Electrical conductivity Electrical percolation Film formation Mathematical modeling Polymer nanocomposites Polymer physics Regression Soft computing Electrical conductivity Polymer physics Regression Film formation Mathematical modeling Artificial neural network Soft computing Bayesian regulation Polymer nanocomposites Electrical percolation
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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