Curve fitting in MATLAB is a powerful technique for modeling relationships between variables and analyzing data trends. It allows you to find the best mathematical function that describes your data points.
MATLAB offers several methods for curve fitting, ranging from simple polynomial fits to more complex custom equations. The most common approach is using the polyfit function for polynomial fitting.
To fit a polynomial to your data, use the polyfit function:
x = [0 1 2 3 4 5];
y = [0 0.8 2.7 5.2 7.8 11.5];
p = polyfit(x, y, 2); % Fit a 2nd-degree polynomial
The resulting p contains the coefficients of the polynomial. You can then use polyval to evaluate the fitted curve:
x_fit = linspace(0, 5, 100);
y_fit = polyval(p, x_fit);
plot(x, y, 'o', x_fit, y_fit, '-');
For more complex relationships, you can define custom equations and use MATLAB's optimization tools to fit them to your data.
fun = @(b,x) b(1) * exp(b(2) * x);
b0 = [1, 0.1]; % Initial guess
b = lsqcurvefit(fun, b0, x, y);
MATLAB's Curve Fitting Toolbox provides a comprehensive set of tools for curve and surface fitting. It offers a graphical interface and additional fitting options:
To launch the Curve Fitting App, type cftool in the MATLAB Command Window. This opens a graphical interface where you can import data, select fitting models, and visualize results.
Curve fitting in MATLAB is widely used in various fields:
By mastering curve fitting techniques in MATLAB, you'll be equipped to extract meaningful insights from your data and create accurate predictive models.