Journal of Materials Science

, Volume 44, Issue 12, pp 3156–3164 | Cite as

Artificial neural network prediction of glass transition temperature of fluorine-containing polybenzoxazoles

  • Liwei NingEmail author


Fluorine-containing polymers belong to high-performance polymers with unique chemical and physical properties that are not observed with other organic polymers. In this article, three structural parameters were used to correlate with glass transition temperature Tg values for 52 fluorine-containing polybenzoxazoles. The descriptors obtained directly from the structures of repeating units can reflect the chain stiffness (or mobility). Back propagation artificial neural network (ANN) and multiple linear regression (MLR) analysis were used in the study. The final optimum neural network with [3-1-1] structure produced a training set root mean square (rms) error of 2.35 K (R = 0.980) and a test set rms error of 2.30 K (R = 0.978). The statistical results indicate that the ANN model given here has better predictive capability than other existing models.


Artificial Neural Network Artificial Neural Network Model Multiple Linear Regression Model Benzoxazoles Significant Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.College of Mechanical EngineeringHunan Institute of EngineeringXiangtanChina

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