Solve Compartmental Model with lsqcurvefitting
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Hi,
My question is about the following convolution:
C(t) = Ca(t) x K1*exp(-k2*t)
where x is the convolution. I have the x- and y-values of the signals C(t) and Ca(t). With a least squares estimation, I want to estimate the parameters K1 and k2.
I started this with the function lsqcurvefitting, for which I first set up the following function, like the example in the help file
function F = myfun(x,xdata)
F = (x(1)*exp(x(2)*xdata));
But, the problem is that I have two sets of xdata and two sets of ydata (from C(t) and Ca(t)). Is this problem even possible to solve with the lsqcurvefitting? because if I want to solve this with the following code, it does not like the function at all (and that is because I totally skipped the convolution with Ca(t), but I don't know how to implement this).
K0 = [0.02; 0]; % Starting guess
[K,resnorm] = lsqcurvefit(@myfun,K0,xdata,ydata)
Btw, the xdata and ydata mentioned in this script is from the C(t) dataset.
Hope this problem is clear to you. Thanks in advance :)
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Answers (1)
Matt J
on 7 Oct 2013
For a fixed k2, you should be able to do a numerical convolution using TRAPZ within your F(x,xdata) function.
Since your K1 function only affects the objective function linearly, you might also consider using
instead of LSQCURVEFIT.
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