基于梯度增强算法CatBoost的聚合物流变参数预测

All the Materials. Encyclopedic Reference Book. Pub Date : 1900-01-01 DOI:10.31044/1994-6260-2023-0-6-21-29

A. Chepurnenko, Tatiana Kondratieva, Timur Deberdeev, V. Akopyan, A. Avakov, V. Chepurnenko

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引用次数: 1

摘要

本文讨论了用CatBoost机器学习算法从应力松弛曲线确定聚合物流变参数的问题。该模型是用非线性Maxwell-Gurevich方程构造的理论曲线来训练的。并与经典算法、非线性优化方法和人工神经网络等方法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Prediction of rheological parameters of polymers, using gradient boosting algorithm CatBoost

The article deals with the problem of determining the rheological parameters of polymers from stress relaxation curves using the CatBoost machine learning algorithm. The model is trained on theoretical curves constructed using the non-linear Maxwell-Gurevich equation. A comparison is made with other methods, including the classical algorithm, non-linear optimization methods and artificial neural networks.

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All the Materials. Encyclopedic Reference Book.

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