A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*

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A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*

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- A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*
- A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*
- 高分子科学（英文版） 2002年20卷第1期页码：25-30
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- 纸质出版：2002-02-20
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A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*[J]. 高分子科学（英文版）, 2002,20(1):25-30.

A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*[J]. 2002, 20(1): 25-30.
A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*[J]. 高分子科学（英文版）, 2002,20(1):25-30. DOI：

A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS*[J]. 2002, 20(1): 25-30. DOI：

摘要

In this paper

an atificial neural network model is adopted to study the glass transition temperature of polymers. Inour artificial neural networks

the input nodes are the characteristic ratio C∞

the average molecular weigh Me betweenentanglement points and the molecular weigh Mmon of repeating unit. The output node is the glass transition temperature Tg

and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting the outcome for polymers for which it is not trained.The maximum relative error for predicting of the glass transition temperature is 3.47%

and the overall average error is only 2.27%.Artificial neural networks may provide some new ideas to investigate other properties of the polymers.

Abstract

In this paper

an atificial neural network model is adopted to study the glass transition temperature of polymers. Inour artificial neural networks

the input nodes are the characteristic ratio C∞

the average molecular weigh Me betweenentanglement points and the molecular weigh Mmon of repeating unit. The output node is the glass transition temperature Tg

and the overall average error is only 2.27%.Artificial neural networks may provide some new ideas to investigate other properties of the polymers.

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