This document discusses nonlinear curve fitting in Matlab. It describes how the Curve Fitting Toolbox uses nonlinear least squares to fit nonlinear models to data, such as Gaussians, ratios of polynomials, and power functions. The process of fitting a surface to data involves opening the fitting tool, selecting and importing data, refining the fit, removing outliers, selecting validation data, and exploring and customizing plots. Different fits can be applied and viewed in the residuals plot to analyze the quality of the fit. More tutorials on nonlinear methods can be found on the provided website.

1. Matlab:Non Linear Methods

2. Nonlinear Least Squares Curve Fitting Toolbox software uses the nonlinear least-squares formulation to fit a nonlinear model to data. A nonlinear model is defined as an equation that is nonlinear in the coefficients, or a combination of linear and nonlinear in the coefficients. For example, Gaussians, ratios of polynomials, and power functions are all nonlinear.

3. Interactive fitting The process of fitting a surface to data involves the following steps: Opening the Surface Fitting Tool Selecting Data Refining Your Fit Removing Outliers Selecting Validation Data Exploring and Customizing Plots

4. 1. Opening the curve fitting tool Type cftoolin the matlab command window

5. 2. Selecting Data Type load census for this example. Then import the data as follows

6. 3. Fitting Use the ‘Fit’ option as follows:

7. 4. Viewing Go to View -> Residual -> Line plot

8. 4. Viewing

9. 4. Viewing Apply different fits and see the result

10. 4. Viewing

11. 5. Analyzing the Fit

12. 5. Analyzing the Fit

13. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net