数据驱动的聚合物分类使用BiGRU和混合元启发式优化算法
Mohammad Anwar Parvez1, Ibrahim M Mehedi2
1Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Polymers
|July 30, 2025
概括
一个新的数据驱动的聚合物分类模型,OADLNN-DDPC,使用深度学习和优化算法来准确识别聚合物类型. 这种先进的方法显著改善了材料科学应用的现有技术.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 传统的聚合物分类方法是劳动密集型的,容易出现错误.
- 越来越需要高效,数据驱动的方法来探索聚合物的广化学空间.
- 深度学习 (DL) 模型为材料科学中的自动化分析和分类提供了强大的工具.
研究的目的:
- 提出一个新的优化算法与数据驱动聚合物分类 (OADLNN-DDPC) 模型的基于深度学习的神经网络.
- 为了提高数据驱动的聚合物分类的准确性和效率.
- 为了利用先进的优化算法来改善聚合物表征.
主要方法:
- 使用Z分数规范化的数据规范化.
- 使用白搜索 (BES) 算法进行特征选择.
- 聚合物分类采用双向门循环单元 (BiGRU) 技术.
- 模型调整使用斑马优化算法 (ZOA).
主要成果:
- 在19500个记录和2048个特征的数据集上,OADLNN-DDPC模型实现了98.58%的高精度.
- 性能优于现有模型,包括LSTM (83.37%),PLS-DA (88.18%) 和K-NN (98.36%).
- 与其他已知方法相比,在聚合物分类性能上显著改善.
结论:
- 拟议的OADLNN-DDPC模型为数据驱动的聚合物分类提供了一种优越的方法.
- 整合DL和优化算法有效地解决了聚合物材料分析中的挑战.
- 这种数据驱动的方法为更准确,更有效地发现新型聚合物铺平了道路.
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