Gaussian Fitting Implementation in MATLAB

Gaussian fitting mentioned in this context is a methodology implemented in MATLAB for data fitting. This technique employs Gaussian functions to approximate data patterns and can be broadly applied across multiple disciplines. In scientific research and engineering fields, Gaussian fitting is commonly utilized for curve fitting applications including spectral analysis (optical spectra, energy spectra, acoustic spectra), and data processing tasks such as data cleaning and outlier detection. The implementation typically involves MATLAB's curve fitting toolbox or custom scripts using fundamental functions like: - `fit()` function with 'gauss1' through 'gauss8' models for single to multiple Gaussian peaks - `normpdf()` for probability density function calculations - Optimization algorithms for parameter estimation (mean, standard deviation, amplitude) - Key steps include data preprocessing, initial parameter guessing, iterative optimization, and goodness-of-fit evaluation using metrics like R-squared This makes Gaussian fitting an invaluable tool that enables researchers and engineers to better understand and process complex datasets through robust mathematical modeling. The method's effectiveness stems from Gaussian functions' ability to naturally describe many real-world phenomena governed by normal distributions.