Curve Fitting Toolbox 1.2.2

Product Description

• Introduction and Key Features
• Working with the Curve Fitting Toolbox
• Previewing and Preprocessing Data
• Developing, Comparing, and Managing Models
• Fitting Models and Methods
• Analyzing Fits

Fitting Models and Methods

The Curve Fitting Toolbox provides a library of linear, nonlinear, and nonparametric fitting models, including:

• Polynomial (to ninth degree)
• Exponential
• Rational (to degree 5/5)
• Peak (Gaussian)
• Distribution (Weibull)
• Fourier and power series
• Spline (cubic and smoothing)
• Interpolant (linear, nearest-neighbor, cubic-spline, shape-preserving)

All library models provide optimized solver parameters. Some nonlinear models compute optimized starting points. You can override default parameters, as well as develop your own set of custom models. In addition, you can save and reload an unlimited number of custom models for repeat use or further modification.

The Curve Fitting Toolbox provides a variety of fitting methods, including:

• Linear least squares
• Nonlinear least squares via trust region
• Levenberg-Marquardt
• Gauss-Newton algorithms

You can assign both weights and bounds on your coefficients. You can also choose two forms of robust fitting: bisquare or least absolute residual.



Custom linear and nonlinear models, developed using the Custom Equation Tool. The linear model is a Legendre polynomial (left), and the nonlinear model is a Fourier series(right) with an unknown frequency parameter. Click on image to see enlarged view.