人工神经网络设计参数对PS/TiO2纳米纤维直径预测的影响
R Seda Tığlı Aydın1, Fevziye Eğilmez1, Ceren Kaya1
1Department of Biomedical Engineering, Zonguldak Bülent Ecevit University, Incivez, Zonguldak 67100, Turkey.
Polymers
|February 13, 2026
概括
人工神经网络 (ANN) 可以准确预测聚钢和PS/TiO2纳米纤维的直径. 优化的多层感知子 (MLP) 和辐射基函数 (RBF) 模型通过精确的结构特征预测来推进材料设计.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 计算建模 计算建模
背景情况:
- 电是制造聚合物纳米纤维的关键技术.
- 预测纳米纤维直径对于定制材料特性至关重要.
- 人工神经网络 (ANN) 为材料科学中的预测建模提供了潜力.
研究的目的:
- 开发和优化ANN模型,用于预测聚乙烯 (PS) 和PS/TiO2纳米纤维的直径.
- 为了比较多层感知子 (MLP) 和辐射基函数 (RBF) 架构的性能.
- 建立一个数据驱动的框架,用于合理的材料设计和合成.
主要方法:
- 通过电制造PS和PS/TiO2纳米纤维.
- 纤维直径的定量表征.
- 开发使用系统和过程参数作为输入的MLP和RBFAN.
- 优化ANN架构,包括隐藏层神经元计数.
- 使用平均平方误差 (MSE) 度量计进行验证.
主要成果:
- 优化的MLP模型实现了4.03 × 10−3 (第1类) 和7.01 × 10−3 (第2类) 的MSE.
- 优化的RBF模型实现了1.42 × 10−32 (第1类) 和2.75 × 10−32 (第2类) 的显著较低的MSE.
- ANN模型的性能高度依赖于架构优化.
结论:
- 优化ANN框架是预测纳米结构材料结构特征的强大工具.
- 该研究强调了数据驱动建模中方法论严谨的重要性.
- 这些预测能力支持合理的材料设计和纳米纤维的合成.
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