A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings
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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 (C_s), initiator concentrations (C_i), molecular weights (M_PS) and particle sizes of PS latex (D_PS) together with MWCNT concentrations (R_MWCNT) 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, R^2, 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 R_MWCNT 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.
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