Adding a condition to the non-linear least square curve fitting

Hello everyone,

I have a question regarding curve fitting using nonlinear regression.

My aim is to calculate three model parameters (a,b,c) out of measurement data to create a physical model. It is a polynomial model with the equation: y=a*x^2+b*x+c

To ensure the physical meaningfulness, the parameters must furthermore fulfill the condition: a+b+c=1

My present code is:

options = fitoptions('Method', 'LinearLeastSquares')

[f_p_zip,g_p_zip]=fit(v_pu,p_pu,'poly2',options);

Unfortunately I cannot find a way to add the condition into the regression process.

Have you an idea how to solve this problem?

Thank you very much in advance,

Kind regards,

Steffi

[Alex Sha]

Hi, take "c=1-a-b" into your fitting function, the your function will be become as: y=a*x^2+b*x+1-a-b, in such case, you don't need to consider any other condition.

[Stefanie Lösch]

Thank you Alex.

This is a very good and elegant way to solve the problem!

Best regards, Steffi