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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
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用反向传播人工神经网络算法对合金进行定量分析方法的研究,基于LIBS技术.

Li Wang1, Li Xu2, Li Li3

  • 1Mathematics and Physics College, Bengbu University, Bengbu 233030, China.

The journal of physical chemistry. A
|April 11, 2025
PubMed
概括

本研究介绍了一种使用激光诱导分解光谱 (LIBS) 和反向传播人工神经网络 (BP-ANN) 的定量模型,用于5052个合金中的快速元素分析. 开发的方法准确地确定了元素度,提高了合金的质量控制.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 分析化学 分析化学
  • 频谱学是一种光谱学.

背景情况:

  • 快速的元素分析对于合金的分类和质量保证至关重要.
  • 现有的方法可能缺乏实时工业应用所需的速度或准确性.

研究的目的:

  • 开发一种定量模型来分析5052 Al-Mg合金中的元素分布.
  • 将激光诱导分解光谱 (LIBS) 与反向传播人工神经网络 (BP-ANN) 结合起来,以提高分析性能.

主要方法:

  • 利用LIBS从5个5052个Al-Mg合金样本 (共940个光谱) 中收集光谱数据.
  • 使用700个培训和225个测试数据集开发了一个BP-ANN模型.
  • 聚焦于396.15 nm的 (Al) 和279.54 nm的 (Mg) 的光谱线.

主要成果:

  • 该BP-ANN模型在预测Al和Mg度方面取得了很高的准确性.
  • 的确定系数为0.9862,的确定系数为0.9646.
  • 观察到较低的平方根平均误差 (0.6609为Al,0.75005为Mg) 和平均绝对误差 (1.0986为Al,0.5504为Mg).

结论:

  • 结合LIBS和BP-ANN方法为合金的定量元素分析提供了一个准确而稳定的方法.
  • 这项技术有助于快速在线分析,支持工业质量控制和材料分类.
  • 该模型显示了金属材料实时元素组成确定的巨大潜力.