| 基于高斯过程回归的橡胶玻璃化温度的预测研究 |
| Study on Prediction of Glass Transition Temperature of Rubber Based on Gaussian Process Regression |
| 投稿时间：2021-02-26 修订日期：2021-02-26 |
| DOI：10.12136/j.issn.1000-890X.2022.11.0826 |
| 中文关键词: 高斯过程回归 反向传播神经网络 玻璃化温度 溶聚丁苯橡胶 |
| 英文关键词: Gaussian process regression BP neural network glass transition temperature SSBR |
| 基金项目:国家自然科学基金资助项目（61873022） |
|
| 摘要点击次数: 1032 |
| 全文下载次数: 922 |
| 中文摘要: |
| 以实例数据为样本，利用高斯过程回归分析苯乙烯和丁二烯含量对溶聚丁苯橡胶（SSBR）玻璃化温度（Tg）的影响，从而预测SSBR的Tg。结果表明：高斯过程回归建立的SSBR的Tg预测模型可靠和有效；与反向传播神经网络模型相比，高斯过程回归模型解决小样本问题具有优越性，可为更多复杂耗时的试验数据预测提供有效解决方案，对一定范围内的苯乙烯和丁二烯含量对SSBR的Tg的影响进行定性和定量分析。 |
| 英文摘要: |
| 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. |
|
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
