Hello, If the fixed point has to be optimized as well, it is possible to keep a linear treatment, although the solution that I have found is tedious: First, notice that because of the fixed point and the set of xk is the same for the 3 lines, all Y coordinates are proportional, I mean
- y2(xk)=P2/P1*y1(xk) - y3(xk)=P3/P1*y1(xk) It is probably easy to fit the datasets y2 and y3 as a function of y1 to find r=P2/P1 and s=P3/P1. It might even be possible to use r=sum(y2)/sum(y1) and s= sum(y3)/sum(y1)... but the exact solution of the least square method is r=sum(y2.*y1)/sum(y1.*y1), s= sum(y3.*y1)/sum(y1.*y1). Then the full dataset of the 3 functions y1, y2/r and y3/s can be adjusted to the same function p1*x+A (e.g. using reglin) However, I did not write the code, yet... There might exist a more elegant solution... I understand it is not in the focus of the initial question, but it may help anyway. Denis NB: a more compact algorithm is to fit for i=1...3, yi/sum(yi.*y1)= f(x) De : users <[email protected]> De la part de Rafael Guerra Envoyé : mardi 25 août 2020 01:47 À : Heinz Nabielek <[email protected]>; Users mailing list for Scilab <[email protected]> Objet : Re: [Scilab-users] errors (uncertainties) in non-linear least-squares fitting parameters In that case, the code can be simplified using backslash left matrix division: // Fixed point (-4,0) solution: a = (MW+4)\Y; b = a*4; GG= a'.*.xx' + repmat(b',1,size(xx,1)); plot(xx,GG','LineWidth',1); Regards, Rafael
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