Two-Dimensional Fitting Function Expression

In this application, we utilize MATLAB's two-dimensional fitting function expression for nonlinear least squares fitting. Specifically, we implement the lsqcurvefit(@myfun, a0, data, zdata) function to achieve this objective. This function employs an optimization algorithm to determine optimal parameter values that minimize the sum of squared residuals between the model predictions and observed data. The @myfun parameter represents the user-defined fitting function containing the mathematical model to be fitted, while a0 specifies the initial parameter guesses. The data variable contains the independent variable inputs, and zdata holds the corresponding dependent variable measurements. Through iterative refinement of parameters, this method identifies the best-fit function expression that most accurately characterizes the input data's underlying patterns. This approach enables deeper insights into data relationships and facilitates model generalization across broader datasets. The implementation typically involves defining appropriate termination tolerances and maximum iteration counts to balance computational efficiency with fitting precision.