 # Software FindGraph Fitting

## Curve fitting and equation discovery

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Discover the best model to describe your data FindGraph is an engineering and science tool that simplifies the task of curve fitting and regression analysis. What sets FindGraph apart from similar curve fitting and regression programs is its ease of use. FindGraph supplies 10 generic fits, including Linear Regression, Logistic Functions, Fourier approximation, Neural Networks plus a library of over 200 built-in functions selected from different areas of research and development.

The Wizard of Approximation will help you to apply a variety of curve fits to your plot to discover the best model to describe your data. With FindGraph, even a statistical novice can find the best equation in just a few minutes. Utilize one of predefined curve fits or enter your own equation to fit your data. All fits are calculated with incredible speed and accuracy. You can fit data to lines or curves. Regression curve fits are 1st to 20th order polynomial, power, exponential, or log. The program uses several non-linear least-square curve fitting algorithms. The simplex and Levenberg-Marquardt algorithms were used for quick nonlinear regression performance. FindGraph enables you to take almost full control of the fitting process: Set the fitting data range; Select the weighting method; Select the best initial parameter values by plotting the curve before the actual fitting and comparing it to the data; Fix and vary the values of parameters.
You can input desirable formula (analytical function) and vary the number of parameters. Once determined the fit parameters, it's possible to extrapolate the fit function and the graph of the obtained function is shown.

Neural network approximation is equally suited for advanced and inexperienced users. You can use your own curve fitting algorithms as plug-ins. We shipped samples (with source code, pure C). Our developers do custom work. It is possible to include your own equations and curve fitting models.

FindGraph uses parametric equations X(u), Y(u) to fit data with closed curves.

Interpolation options include Lagrangian polynomials and Cubic splines. Interpolations and curve fitting in polar coordinates are possible. Digitizing Graphing Linear regression Closed curves Empirical models Library Analysis Tools Filters Convolution Periodic signals Procrustes analysis Multi-peak fitting SSA forecasting

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