I have an array A,
now i have fitted array A to an objective function objfcn = @(b,x) b(1).*x.^b(2) + b(3).*x.^b(4); as below:
B0 = ones(4,1);
[B,rsdnrm] = fminsearch(@(b) norm(A(:,2) - objfcn(b,A(:,1))), B0);
fprintf(1, 'c_1 = %12.6f\nc_2 = %12.6f\nn_1 = %12.6f\nn_2 = %12.6f\n', B)
and i am satisfied with the fit. However, fminsearch method does not give errors on parameters (b(1),b(2),b(3),b(4)). I tried other methods such as ''lsqnonlin'' and "lsqcurvefit ", but they do not reproduce the same parameters that i obtain from fminsearch. I was wondering if anyone knows a robust nonlinear least square fit method that is able to estimate parameter error?
Thank you all