论文标题
快速准确的线性拟合，用于不完全采样的高斯功能，长尾巴
Fast and Accurate Linear Fitting for Incompletely Sampled Gaussian Function With a Long Tail
论文作者
论文摘要
将实验数据拟合到曲线是一种通用信号处理技术，可提取数据特征并确定变量之间的关系。通常，我们期望曲线符合某些分析函数，然后将数据拟合变成估计函数的未知参数。在用于数据拟合的分析功能中，高斯功能是最广泛使用的功能，因为它在众多科学和工程领域中的广泛应用。仅举几例，高斯功能在统计信号处理和分析中非常流行，这要归功于中心限制定理[1]；高斯函数经常出现在量子谐波振荡器，量子场理论，光学，激光器以及许多其他物理学的理论和模型中[2]；此外，高斯功能被广泛用于化学中，用于描绘分子轨道，计算机科学用于成像处理和定义神经网络的人工智能。
Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1]; Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in Physics [2]; moreover, Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing and in artificial intelligence for defining neural networks.
