f = @(x,b) b(1)*(1-(1+b(2)*x/2).^(-2))

dim = 2 
% dimension of the minimization problem
nb = 14 
% number of points

% NIST certified values
target = zeros(dim,1)
target(1) = 3.3799746163E+02
target(2) = 3.9039091287E-04

% start 1
ig1 = [500.;0.0001]
% start 2
ig2 = [300.;0.0002]

% points to fit
x = zeros(nb,1)
y = zeros(nb,1)
x(1)=77.6E0;		y(1)=10.07E0;
x(2)=114.9E0;		y(2)=14.73E0;
x(3)=141.1E0;		y(3)=17.94E0;
x(4)=190.8E0;		y(4)=23.93E0;
x(5)=239.9E0;		y(5)=29.61E0;
x(6)=289.0E0;		y(6)=35.18E0;
x(7)=332.8E0;		y(7)=40.02E0;
x(8)=378.4E0;		y(8)=44.82E0;
x(9)=434.8E0;		y(9)=50.76E0;
x(10)=477.3E0;		y(10)=55.05E0;
x(11)=536.8E0;		y(11)=61.01E0;
x(12)=593.1E0;		y(12)=66.40E0;
x(13)=689.1E0;		y(13)=75.47E0;
x(14)=760.0E0;		y(14)=81.78E0;

scatter(x,y)

rng(0)	
% for tractability 
[b,info] = lsqcurvefit(f,ig1,x,y)	
% running minimization 

rng(0)
[~,info] = lsqcurvefit(f,ig1,x,y)
info.plot = 'fit'
info.animate = true	
% plot animation 
optimview('lsqcurvefit',info) 

References

Resources

See also

Related functions