| Study on Prediction of Glass Transition Temperature of Rubber Based on Gaussian Process Regression |
| Received:February 26, 2021 Revised:February 26, 2021 |
| DOI：10.12136/j.issn.1000-890X.2022.11.0826 |
| Key Words: Gaussian process regression；BP neural network；glass transition temperature；SSBR |
| Author Name | Affiliation | E-mail | | CHEN Zhudan | Beijing University of Chemical Technology | lidz@mail.buct.edu.cn | | LI Dazi* | Beijing University of Chemical Technology | lidz@mail.buct.edu.cn | | Liu Jun | Beijing University of Chemical Technology | | | GAO Ke | Beijing University of Chemical Technology | |
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| Abstract: |
| Taking the example data as the sample，the influence of styrene and butadiene contents on the glass transition temperature （Tg） of solution-polymerized styrene-butadiene rubber （SSBR） was analyzed
by using the Gaussian process regression，so then pIn this study，the influence of styrene and butadiene contents on the glass transition
temperature（Tg） of solution-polymerized styrene-butadiene rubber（SSBR） was analyzed by using Gaussian
process regression，and the method to predict the Tg of SSBR was established. The results showed that，the Tg
prediction model of SSBR established by Gaussian process regression was feasible and effective. Compared
with the back propagation（BP） neural network model，the Gaussian process regression model had advantages
in solving the small sample problem，and could provide an effective solution for more complex and timeconsuming
test data prediction. It was effective in the qualitative and quantitative analysis of the influence of
styrene and butadiene content in a certain range on the Tg of SSBR. |
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