I'm attempting to run experimental data through the isqcurvefit tool in order to obtain a value for alpha from my fit. The function I'm trying to fit approximates to is a summation but it works accurately for four summations so I've actually written it out to this order as the function. The function is:
function F = alpcal(alpha, t)
F = 1 + 2*(((-1).^(1)).*exp(-((1.^2).*(pi^2).*(alpha.*t))/((1e-3)^2))) + ...
1 + 2*(((-1).^(2)).*exp(-((2.^2).*(pi^2).*(alpha.*t))/((1e-3)^2))) + ...
1 + 2*(((-1).^(3)).*exp(-((3.^2).*(pi^2).*(alpha.*t))/((1e-3)^2))) + ...
1 + 2*(((-1).^(4)).*exp(-((4.^2).*(pi^2).*(alpha.*t))/((1e-3)^2)));
For each run I obtain:
Norm of First-order
Iteration Func-count f(x) step optimality CG-iterations
0 2 7340.11 0
Initial point is a local minimum.
Optimization completed because the size of the gradient at the initial point
is less than the selected value of the function tolerance.
in the command window.
This is the attempt I've made to correct this, but it hasn't made any difference, I continue to get that result.
options = optimset('DiffMinChange',1e-3,'disp','iter','TolFun',1e-5, ...
'DiffMaxChange',1);
A = @(alpha, t)alpcal(alphat, t, L);
[alp,resnorm,residual,exitflag] = lsqcurvefit(A, alphat, t, y);
I can't say that I understand entirely what the options functions do, so I'm stabbing in the dark a bit at the moment and going off what I've managed to find after extensive internet searching. But if anyone can let me know where I'm going wrong it would be greatly appreciated as I've spent quite some time searching.
