Riccardo,

minima are the maxima of the negative function! Let data be your data from above, then you can do:

maxIndx = First /@ FindPeaks[Last /@ data];
minIndx = First /@ FindPeaks[Last /@ -data];
ListLinePlot[data, ImageSize -> Large, Epilog -> {PointSize[.01], Red, Point[data[[maxIndx]]], Green, Point[data[[minIndx]]]}]

which gives:

Regards -- Henrik

You need to understand the model for your data, you can not just put almost random arbitrary rough functions and expect it to work. It is like fitting circle with a square and complaining it does not work. Nice advice given to you here by other member assumes that you will put more work into the details of the fit. Did you read any literature on fitting, and do you understand the mechanism behind your data? Many functions can fit your data, but they can be really far from the real mechanism behind the data, so they loose meaning. Are you sure you need an analytic model at all, maybe Interpolation would do for your goals? I got this "example" fit, notebook is attached. It fits twice: first a simple model, then fits residues of a simple model. It works with rescaled data: over x and y, you can easily rescale back. Rescaled data are easier to understand.

Attachments:

Thank you all i try now

Riccardo, try Sinc[x] instead of Sin[x]/x.

Regards -- Henrik