Problem using function with fminsearch

I have a set of data that I want to perform a fit to (data X and Y). I want to fit this data to a function Yfit = A / (1+(X/B)), where A and B are coefficients I want to find by minimising the sum of the squared residuals using fminsearch .

Here’s what I am trying at the moment: First, in a separate .m file I define the Yfit function:

function[res] = Yfit_func(A,B,X)
Yfit = A/(1+(X/B));
res = sum((Y-Yfit).^2);
end

As I understand, that defines a function called “Yfit_func”, with an output called “res”, and inputs “A”, “B” and “X”. “res” is the residuals term to be minimised to find A and B.

In the main .m file I call the fitting function using:

Coefficients = fminsearch(@Yfit_func, guess,[], Y, X);

My understanding of that line is that fminsearch calls the function "Yfit_func", uses the values contained in "guess" as starting points for finding the values of A and B that minimise "res" when fitting the function in "Yfit_func" to the data contained in X and Y.

The error I get is: “Error using / Matrix dimensions must agree. Error in Yfit_func (line 2) Yfit = A/(1+(X/B));”

Not sure what's going wrong here, any help would be appreciated. Thanks.

Answer 1

[Amit]

Use your function like this

function res = Yfit_func(A,Y,X)
Yfit = A(1)./(1+(X/A(2)));
res = sum((Y-Yfit).^2);
end

and run fminsearch like this:

guess = rand(2,1); % your starting point, You can change this
Coefficients = fminsearch(@(A) Yfit_func(A,Y,X), guess);

fminsearch does not takes input like you have used.