3D Scattered Data Fitting

This article presents the implementation of 3D scattered data fitting using MATLAB programming language. 3D scattered data fitting serves as a crucial data analysis technique widely applicable across various domains including engineering, natural sciences, and biomedical fields. We will elucidate the fundamental principles, methodologies, and techniques underlying 3D scattered data fitting. The implementation covers comprehensive MATLAB programming aspects: data ingestion using functions like load or readtable, data preprocessing through normalization and outlier removal, appropriate model selection utilizing interpolation methods (griddata, scatteredInterpolant) or regression algorithms, parameter optimization via least-squares fitting, and result evaluation using metrics like RMSE and visualization techniques. The article includes practical case studies demonstrating complete implementation workflows, helping readers better comprehend and apply 3D scattered data fitting techniques in real-world scenarios.