Multiparameter nonlinear multi-curve fit How to?

[mzu]

(xpost: http://lavag.org/topic/11870-multiparameter-nonlinear-multi-curve-fit/) 

There is a model of a physical process formalized in LabVIEW simulation toolkit. It take a set of parameters, and produces time dependencies of observables. I have an experimental data, same time dependencies of observables, but measured on a real system. I would like to get the set of parameters, so that model curves will fit experimental. I have an idea of the initial values of the parameters. Sample of data is provided below:





Points are experimental data, curves are the results of manual fitting, using the above mentioned model.

Had there been one curve, it would have been easy. I would have used NonlinearCurveFit.vi, and supply it with a reference to a model curve generating VI. What can I do with multiple curves? I really would like to avoid porting the fitting

algorithm from the Numerical Recipes. I would also like to avoid writing a wrapper (OriginC <-> LabVIEW) to do a linked fit in Originlab Origin. I can use Matlab, of course, but it will require writing another wrapper. Is there a quick way of doing it, without leaving LabVIEW?

[Darin.K · Accepted solution]

One way to do this is to "stack" your different curves into a single 1D array and fit them.  Here is an example of fitting a*exp(-x/x0) and a(1-exp(-x/x0)) simultaneously.  In exponentials.vi I take a single x array and output concatenated arrays for f(x,a) as well as the derivatives.  In the curve fitting routine, there is no requirement that x be single valued or continuous, or ordered, etc.  It is just a pile of data, and as long as you can properly calculate a guess for each data point you are alright.

Message Edited by Darin.K on 02-03-2010 11:40 AM

[mzu]

Great idea, will try.

[av76]

Can you convert this to LabView 8.6?

Thanks in advance.

[Darin.K]

In LV8.6