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Re: Fitting experimental data

  • To: mathgroup at smc.vnet.net
  • Subject: [mg85525] Re: Fitting experimental data
  • From: dh <dh at metrohm.ch>
  • Date: Wed, 13 Feb 2008 04:18:55 -0500 (EST)
  • References: <fopb53$bhu$1@smc.vnet.net>


Hi Kevin,

try to define "simulatedData" more spezifically, so that it does not 

evaluate with symbolic parameters. E.g:

simulatedData[xaxis_List,testInstFunc,k1auto_?Numeric?,a1initauto_?NumericQ]

hope this helps, Daniel



Ausman, Kevin wrote:

> 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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