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神经网络的多目标优化,用于预测织聚合物复合材料的物理性质

Ivan Malashin¹, Vadim Tynchenko¹, Andrei Gantimurov¹

¹Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.

|June 27, 2024

概括

这项研究优化了人工神经网络 (ANN) 和支持向量机器 (SVM),使用多目标优化来预测织聚合物复合材料的性能. 这提高了材料的设计和开发.

科学领域:

材料科学材料科学材料科学
计算科学计算科学
工程工程师工程师工程师

背景情况:

预测织聚合物复合材料 (TPCM) 的物理性能对于先进的材料设计至关重要.
人工神经网络 (ANN) 和支持向量机器 (SVM) 是有前途的,但需要精确的超参数调整以获得最佳性能.
现有的方法可能无法完全捕捉构成材料属性和最终复合材料行为之间的复杂关系.

研究的目的:

应用多目标优化算法 (MOPSO,NSGA II,SPEA2) 调整ANN和SVM的超参数.
通过使用纤维和织物数据,提高ANN和SVM对TPCM的预测准确度.
通过增强的预测建模,促进高性能TPCM的开发.

主要方法:

使用的多目标优化算法:多目标粒子群优化 (MOPSO),非主导排序遗传算法II (NSGA II) 和强度帕雷托进化算法2 (SPEA2).
应用这些算法来优化人工神经网络 (ANN) 和支持矢量机器 (SVM) 的超参数.
输入数据包括TPCM构成纤维和面料的物理特性.

主要成果:

证明了MOPSO,NSGA II和SPEA2在优化TPCM的ANN和SVM超参数方面的成功应用.
与基线模型相比,实现了TPCM的增强预测精度.
通过比较分析和实验数据验证了优化方法的有效性.

结论:

多目标优化技术显著提高了ANN和SVM对TPCM的预测能力.
提出的方法为优化复杂材料系统提供了一个强大的框架.
这种方法加速了高性能织聚合物复合材料的设计和开发周期.

关键词:

多目标优化多目标优化神经网络的神经网络的神经网络物理属性预测预测织聚合物复合材料的复合材料

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