MATLAB: Parameters to consider in non linear curve fitting

constrained curve fittingCurve Fitting Toolboxexperimental data fittinglsqcurvefitnon linear curve fitting

I have a complex (sum and multiplication of several elementary functions) function to fit with experimental data, but I could not reach the desired fit, the program does his work but the parameters are far from what they should be, I noticed that if I change the lb or ub it changes the results also if I change Beta0 it also affects the results. I need help and advise on the factors that I should consider to have the best fit

note: the experimental data are about 6000 points, I have around 15 parameters to find

thanks in advance

Best Answer

Without knowing more, I suggest you choose Beta0 to be close to what you expect the parameters to be as you can. I also suggest not restricting the parameters with lb and ub unless you know that, for instance, they must be positive or if you already know they should not exceed a specific value.

Fitting so many parameters will give any solver problems. Consider reducing them if you can.

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The plot command plots only the real parts of the numbers. If you want to plot all your data, you can either plot the absolute magnitude (use the abs function), or plot the real and imaginary parts separately, either in the same plot or using subplot to plot them on different axes in the same figure window.

It may be worth exploring the reason you are getting complex parameters and results. If you can use lsqcurvefit, consider constraining your parameters. (I do not have the Curve Fitting Toolbox, so if you are using it, I cannot help you.)

Best Answer

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