Optimization: find model parameters comparing with experimental data
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Hi, I have a model (*.m) written in Matlab with several parameters. I would like to find the optimal parameters that will give me the minimum error compared to the experimental data.
Basically, I have to minimize this function (let's call it "error"):
error=|mymodel(a,b,c,d....)-expData|
"mymodel" and "expData" are just 1-D arrays. Actually they are 2-D arrays (let's call the columns "x"and "y") but I have done an interpolation of the most dense array ("mymodel" in this case), so that the I get the value of "y" for the 2 arrays at the same value of "x".
How can I do that? I have seen many tutorials but I don't quite understand. I have to make a function and use the optimization toolbox on that function, but how I can define the parameters to change?
Thank you
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Torsten
am 1 Mär. 2016
Reading the description of lsqcurvefit should help:
Best wishes
Torsten.
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