LSF：最小二乘拟合

“最小二乘拟合”（Least Squares Fit）是一种在数学及工程学等领域广泛应用的参数估计方法，通常简写为LSF。这种缩写形式方便学术写作与日常交流，尤其在数据处理与曲线拟合分析中频繁使用，旨在通过最小化误差平方和来寻找数据的最佳函数匹配。

With the nonlinear least squares fit between measured spectra and calibration spectra, standard gas concentrations of CO at different temperatures are obtained.
将合成校准光谱和实验测得的光谱进行非线性最小二乘拟合(LSF)，得到了不同温度下标准气体CO浓度。
Consequently we obtained the analytical transfer functions of two meters with a linear least squares fit.
文中采用线性最小二乘拟合(LSF)方法，求得了两仪器传输函数的解析表达式。
These algorithms include digital low_pass filters, multiple averages, and a least squares fit.
这些信号处理包括多次平均、数字低通滤波以及最小二乘法拟合。
It studies the image processing scheme on meter full-automatic calibration, and the least squares fit method based on maximum likelihood estimate has been detailed description to discriminate needle and dial in the intelligence meter processes.
研究图像处理技术在仪表自动化校验中的应用方案，详细介绍了基于最大似然估计的最小二乘拟合(LSF)方法来确定指针和刻度线的参数。
An arbitrary spectrum was simulated by the least squares fit, using the D-T spectrum and the 200 # spectrum as known spectra.
以D-T中子源和200~反应堆中子源为已知模拟源，对一个任意中子源进行了最小二乘法拟合。

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