Unable to fit S-shaped data to sigmoid function in Curve Fitting Toolbox
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Dear all,
I am trying to fit some experimenta data which has a S-shaped form into a sigmoid function (which is also S-shaped). I am using the generalised logistic function as custom equation in the curve fitting toolbox, but I am unable to get a fit to the data.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/201871/image.png)
The current default curves yield by the matlab toolbox are completly off the data. I have tried different (and more simple logistic functions), different fititing algorithms and different robustness methods. I am unsure how MATLAB computes the fit curve, but playing around with the starting points of the coefficients, sometimes I can get a good fit, eg. starting points: a = b = c = k = 0 and q = 1, yields a relative good fit:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/201872/image.png)
A different set of initial values (eg. starting points: a = b = c = k = 0 and q = 2 or 0.5) yields a completly different fit.
I am seeking a robust way of fitting the data, (I need to do this for thousands sets of data), but I have been struggling to do such a simple fit. It may be the starting point of the fit, the method or even the equation.
Any suggestion for obtaining a robust fit (unsupervised) of S- and Z-shaped data would be extremely welcome.
Daniel.
2 comentarios
Amara Kouyate
el 19 de Mzo. de 2019
Hello,
I am having the same issue. I have a custom equation : 4.5-exp(a-b*c^log(1/x))
I am looking for the exact value for coefficient a, b, and c while minimizing SSE.
But the fitting tool won't work unless I have a good starting point for a, b, and c.
Is it possible to automate that process whitout trying to find the exact starting point for the coefficients?
Or how to choose the exact or appropriate starting point for coefficient so that data can fit the curve using the curve fitting app?
Thank you
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