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