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There are more sophisticated methods for getting the best possible curve fi t for a number of parameters when the curve position is a non-linear funct ion of each of the parameters.
I got exposed to the Marquardt algorithm when I was doing my Ph.D. For my p roblem the Fletcher-Powell approach worked better, and I ended up coding it for myself - in Fortran 4.
I had the advantage that I could work out (and sum) the derivatives of the least squares sum against each parameters at the same time as I was numeric ally integrating the function I was trying to fit to the data, so the proce ss of numerically estimating the second derivatives was a bit more accurat e than it might have been.
You will have a lot more parameters than I had (three - with a brief and un successful excursion into four) but most of your won't make any difference over most of the curve, so you shouldn't have lot of trouble with parameter interaction - where the best fit for one depends heavily on all the others . Marquardt seemed to work better for lots of parameters - my tutor from T heory of Computation 1 had written his M.Sc. on the subject, and that was w hat I got from it when I got to read it back then.