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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Bibliographic Details
Published in:Applied soft computing Vol. 96; p. 106632
Main Authors: Demirbay, Barış, Kara, Duygu Bayram, Uğur, Şaziye
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2020
Subjects:
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 Access:Get full text
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Summary: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 measurements. Surfactant concentrations (Cs), initiator concentrations (Ci), molecular weights (MPS) and particle sizes of PS latex (DPS) together with MWCNT concentrations (RMWCNT) were introduced as inputs while electrical conductivity (σ) was assigned as a single output in FFNN topology. Network training was carried out using a Bayesian regulation backpropagation algorithm. Optimal geometry of the hidden layer was first studied to search out the best FFNN topology providing the most accurate performance results. Mean squared error, MSE, mean absolute error, MAE, root-mean-squared error, RMSE, determination of coefficient, R2, variance accounted for, VAF, and regression analysis were employed as performance assessment parameters for proposed network model. Correlation coefficients (r) of each input variable together with relative importance-based sensitivity analysis results have shown that RMWCNT is the most significant input variable strongly affecting the σ value of PS/MWCNT nanocomposite film coatings and training performance of the neural network. Mathematical explicit function has been derived to model electrical conductivity by using weights and bias values at each neuron found in FFNN development. All predicted conductivity values are in a very good agreement with measured conductivity values, showing robustness and reliability of suggested FFNN model and it can be effectively used to predict electrical conductivity of PS/MWCNT nanocomposite film coatings. •A Bayesian regularized FFNN model was developed to predict electrical conductivity.•Performance assessment results have shown the robustness of the developed FNNN model.•Sensitivity analysis was carried out to determine the most effective input neuron.•A neural predictive formula was derived from interconnection weights and bias values.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2020.106632