Fitting data with two peaks

Question

Niles Martinsen 2012-6-4

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编辑： John Kelly 2015-3-2

Hi

I have some data, which is not too different from the top graph in this picture: <http://www.aanda.org/index.php?option=com_image&format=raw&url=/articles/aa/full/2004/12/aa0526/img14.gif>

In other words, there are two peaks that each represent a Lorentzian. I am not sure how to fit this in MatLAB. Is there a way to fit the data to one function consisting of two Lorentzians, or do I have to split the data set in two, one peak in each?

Ultimately I need to find the x-position of each peak.

Best, Niles.

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Enrique 2014-7-30

Niles,

Did you ever figure out how to do this and/or implement any of the solutions suggested below? I am trying to fit two Lorentzians to similar Raman data as yours (is yours a graphene Raman spectrum as well?). In the past I have done this using a single Lorentzian fitting function I found ( Lorentzian Function Fit lorentzfit), but have not tried to implement two Lorentzians. Any suggestion would help a lot.

Thanks,

Enrique

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Answer 1

Geoff 2012-6-4

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在 MATLAB Online 中打开

Define a function that accepts the parameters of the two lorenzian curves and computes the full curve.

I don't know how many parameters you need cos I'm no mathematician =) Let's say 2?

dualLorentz = @(x, a1, b1, a2, b2) = lorenz(x, a1, b1) + lorenz(x, a2, b2);

Then, define a function to generate that curve, subtract your actual dataset, square the result and sum it.... While you're at it, parameterise the whole thing (ie a vector p of [a1, b1, a2, b2])

objFn = @(p, x, y) sum( (y - dualLorentz(x, p(1), p(2), p(3), p(4))) .^ 2 );

Then chuck it at fsolve or fminsearch - assuming your dataset is in X and Y:

p0 = % some initial guess at a1, a2, b1, b2 : you can probably be very basic
p = fsolve( @(p) objFn(p, X, Y), p0 );

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Answer 2

Image Analyst 2012-6-4

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Maybe try this:

http://www.mathworks.com/matlabcentral/fileexchange/24443-slm-shape-language-modeling

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Answer 3

Ryan 2012-6-4

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编辑：John Kelly 2015-3-2

You could get a close approximation of peak position with a cubic spline fit and local maxima. You could also try just smoothing the data first as well and then finding the maxima.

Cubic Spline:

http://www.mathworks.com/help/curvefit/csaps.html