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