Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks
Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks
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自动拟合高斯峰值的算法和机器学习方法的比较
DOI：
10.1007/s005210200012
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发表时间：
2002
影响因子：
6
通讯作者：
R. Abdel
中科院分区：
文献类型：
--
作者：
R. Abdel
将高斯峰拟合到实验数据在许多学科中都很重要，包括核光谱学。非线性最小二乘拟合方法已经使用了很长一段时间，但这些方法是迭代的，计算密集型的，需要用户干预。机器学习方法可以自动化并加快拟合过程。然而，对于一个单一的纯高斯，存在一个简单的和自动的分析方法的基础上线性化，然后加权线性最小二乘（LS）拟合。本文比较了这种算法方法与基于AIM 1（外展诱导机制）的外展机器学习方法。这两种技术进行了简要介绍，并比较其性能分析模拟和实际的光谱峰。在500个峰上进行评价，统计不确定度对应于峰计数100，AIM的峰高、位置和宽度的平均绝对误差分别为4.9%、2.9%和4.2%，LS为3.3%、0.5%和7.7%。AIM对于宽度更好，而LS对于位置更准确。LS误差更有偏差，低估了峰位置，高估了峰宽度。试验性的CPU时间比较表明AIM的速度优势为5倍，它也具有恒定的执行时间，而LS时间取决于峰宽。
Fitting Gaussian peaks to experimental data is important in many disciplines, including nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long time, but these are iterative, computationally intensive, and require user intervention. Machine learning approaches automate and speed up the fitting procedure. However, for a single pure Gaussian, there exists a simple and automatic analytical approach based on linearisation followed by a weighted linear Least Squares (LS) fit. This paper compares this algorithmic method with an abductive machine learning approach based on AIM 1(Abductory Induction Mechanism). Both techniques are briefly described and their performance compared for analysing simulated and actual spectral peaks. Evaluated on 500 peaks with statistical uncertainties corresponding to a peak count of 100, average absolute errors for the peak height, position and width are 4.9%, 2.9% and 4.2% for AIM, versus 3.3%, 0.5% and 7.7% for the LS. AIM is better for the width, while LS is more accurate for the position. LS errors are more biased, under-estimating the peak position and over-estimating the peak width. Tentative CPU time comparison indicates a five-fold speed advantage for AIM, which also has a constant execution time, while LS time depends upon the peak width.
使用多层神经网络进行光谱峰值验证和识别。
影响因子：
7.4
作者：
Wythoff,BJ;Levine,SP;Tomellini,SA
通讯作者：
Tomellini,SA