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Fitting experimental data

  • To: mathgroup at smc.vnet.net
  • Subject: [mg85462] Fitting experimental data
  • From: "Ausman, Kevin" <ausman at okstate.edu>
  • Date: Mon, 11 Feb 2008 06:12:14 -0500 (EST)

I am trying to fit experimental data (a list of {x,y} points) with a 
model that produces a similar list of points. It seems that FindMinimum 
with a Levenberg Marquardt method makes the most sense; unfortunately, I 
can't seem to get it to work.

soln=FindMinimum[Sum[(xptData[[i,2]]-(simulatedData[xaxis,testInstFunc,
k1auto,a1initauto])[[i]])^2,{i,1,Length[xaxis]}],{{k1auto,0.04},{a1initauto,0.9}}]

In this example, xptData is the list of experimental data. simulatedData 
returns just the y axis as a list (assuming the same x axis), and the 
resulting list, as you might imagine, depends on k1auto and a1initauto. 
However, this results in a number of errors, I think because it tries to 
evaluate simulatedData for the general case rather than for specific 
instances of k1auto and a1initauto. simulatedData requires the use of 
ListConvolve, NDSolve, and a number of other functions that seem to 
preclude a general solution rather than a specific solution.

Does anyone have any thoughts on how to overcome this problem? Thanks!

Kevin Ausman
ausman at okstate.edu


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