MATLAB: Problem using nlinfit function

nlinfit

Hi everyone,

I am having trouble using nlinfit for making a regression with an own function.

I have the following script:

    xdata = 1:150; 
    xdata = xdata';
    ydata = beta;
    beta0 = [0 -1 20];
    n = 1:150;
    y = nlinfit(xdata,ydata,fun,beta0);.

and the function fun.m is this:

    function y = fun(beta0,n);
    y = beta0(1)+ beta(2).*exp(-n/beta(3));

The basic original function that I want to use is a +be^(t/tau) to fit a set of data points:

I have tried a lot, but I get different errors, variables not defined, input must be 4 variables etc. Please help me out!

Best Answer

1. ydata has to be an array of the same size as xdata (I don't know the size of the array beta).

2. The call to nlinfit must read

y = nlinfit(xdata,ydata,@fun,beta0);

3. fun.m must read

 function y = fun(beta0,xdata);
 y = beta0(1)+ beta0(2)*exp(-xdata/beta0(3));

Best wishes

Torsten.

Related Solutions

MATLAB: Data fitting by non linear discrete equation.

The code below uses fminsearch, which means you don't need the optimization toolbox. The downside is that it is sensitive to a local minimum, depending on your initial parameter estimate.

xdata=[5.0	4.2	4.3	4.3	4.3	4.1	3.7 3.6	3.9	5.9	6.7	5.4	5.1	5.5	6.2	6.7	6.9	6.2	6.1	6.3	5.9	5.5	5.5	5.5	5.5];
ydata=[2.0	2.5	1.9	2.1	2.0	2.1	2.7	4.0	3.8	3.9	3.6	3.2	3.2	3.1	3.2	3.7	3.7	3.2	3.2	3.5	3.5	3.2	3.1	3.3	3.1];
initial_guess=0.5*ones(1,5);
fitted_params=fminsearch(@(fit_vals) costfun(fit_vals,xdata,ydata),initial_guess);
[alpha,beta,a,b,c]=deal(fitted_params(1),fitted_params(2),fitted_params(3),fitted_params(4),fitted_params(5));
x0=xdata(1);y0=ydata(1);elems=numel(xdata);
[x_fitted,y_fitted]=f_g(fitted_params,x0,y0,elems);
disp([x_fitted;y_fitted])
  Columns 1 through 17

    5.0000    3.7777    2.9288    2.3394    1.9301    1.6459    1.4485    1.3114    1.2163    1.1502    1.1043    1.0724    1.0503    1.0349    1.0241    1.0161    1.0095
    2.0000    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9821    0.9820    0.9816    0.9799    0.9767

  Columns 18 through 25

    1.0039    0.9990    0.9946    0.9906    0.9871    0.9835    0.9810    0.9765
    0.9734    0.9701    0.9670    0.9639    0.9615    0.9581    0.9577    0.9489
function cost=costfun(fit_vals,xdata,ydata)
x0=xdata(1);y0=ydata(1);elems=numel(xdata);
[x,y]=f_g(fit_vals,x0,y0,elems);
cost= sum((x-xdata).^2) + sum((y-ydata).^2);
[alpha,beta,a,b,c]=deal(fit_vals(1),fit_vals(2),fit_vals(3),fit_vals(4),fit_vals(5));
if ( alpha<0 || alpha>1 ) || ...
        ( a<0 || a>alpha ) || ...
        ( beta<0 || beta>1 ) || ...
        ( b<0 || b>beta ) || ...
        c<0
    cost=inf;
end
end
function [x,y]=f_g(params,x0,y0,elems)
[alpha,beta,a,b,c]=deal(params(1),params(2),params(3),params(4),params(5));
x=zeros(1,elems);x(1)=x0;
y=zeros(1,elems);y(1)=y0;
for n=2:elems
    x(n)=(1-alpha)*x(n-1) + a/(1+exp(-c*(x(n-1)-y(n-1))));
    y(n)=(1-beta )*y(n-1) + b/(1+exp(-c*(x(n-1)-y(n-1))));
end
end

MATLAB: Nlinfit Options – how to set them

doc statset

statset is a function that will create a structure variable of options; you then pass this structure into nlinfit:

opts = statset('MaxIter',600);
fit = nlinfit(xdata,ydata,f,c,opts)

opts is the structure variable.

Best Answer

Related Solutions

MATLAB: Data fitting by non linear discrete equation.

MATLAB: Nlinfit Options – how to set them

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