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Hello,
I tried to do some non-linear fits to my experimental data using lsquares, but I did not succeed till now. I described my problems here (in order not to have too big mails on this list.
http://paste.lisp.org/display/30278
These are some of the functions I would like to fit to my experimental data:
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) * (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) *log(a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) / (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * log(a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) * (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * log(a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) *log(a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n * log(a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) / (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n / (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) * (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n / (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) *log(a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n / (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) *log(a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
lsquares(m, [T, g, n, l], l = 0.01 * g * (a1 * T + b1) + 0.01 * n / (a2 * T + b2) + 0.01 * (1 - 0.01 * (g + n)) / (a3 * T + b3), [a1, a2, a3, b1, b2, b3]);
Are there any better methods of nonlinear fitting inside maxima?
Thanks
Robert