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