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