화학공학소재연구정보센터
Journal of Polymer Science Part B: Polymer Physics, Vol.38, No.23, 3163-3167, 2000
Predicting chain dimensions from an artificial neural network model
Artificial neural network models are used to investigate polymer chain dimensions. In our model, the input nodes are glass transition temperature (T-g), entanglement molecular weight (M-e), and melt density (rho). The number of nodes in the hidden layer is eight. We found that the relative error for prediction of the characteristic ratio ranges from 0.77 to 7.5% and that the overall average error is 3.57%. Artificial neural network models may provide a new method for studying statistics properties of polymer chains.