How to use fminsearch for least square error minimization?

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Hi everyone,
I am doing a Modal Parameter Estimation problem. I have measured values, and a function for numerical values. There is an error, which I need to minimize. But when I use fminsearch, it says that the dimensions on left hand side don't agree with that of right hand side. Becuase, fminsearch only gives 1x2, while the error (objective function) is 1x269.
I have used the following MATLAB commands:
e=@(uk) (abs(data_1(2561:2819,4))-abs((2i.*Hr.*uk(2).*uk(1).*uk(1))./(((uk(1).^2)-(ws.^2) + 2i.*uk(2).*uk(1).*ws))).^2
fminsearch(e,[413.4,0.0034])
Here, ws = 400:0.155:440
Any suggestions? Thank you for your time.
  2 comentarios
Rik
Rik el 25 de Jul. de 2021
You need to design a function that returns a scalar. Then fminsearch will adjust the starting guesses to minimize that function.
Muhammad Affan Arif
Muhammad Affan Arif el 26 de Jul. de 2021
@Rik So you mean, I need to design a function that minimizes the objective function at each data point?

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Respuesta aceptada

Rik
Rik el 26 de Jul. de 2021
Editada: Rik el 27 de Jul. de 2021
I mean your objective function must only return 1 value, regardless of the shape of your data.
This is the standard ordinary least squares cost function. You need to provide a handle to your function, your beta will be determined by fminsearch, and you need to know the true value.
t=linspace(0,2*pi,100);
f=@(beta) sin(beta(1)*t+beta(2));
initial_guess=[1 1];
y_true=linspace(0,10,100);
OLS=@(f,beta,y_true) sum((f(beta)-y_true).^2,'all');
beta_fitted=fminsearch(@(beta) OLS(f,beta,y_true),initial_guess)
beta_fitted = 1×2
-0.0000 7.8540
Edit: sorry, I missed the squared part of the OLS.
  1 comentario
Muhammad Affan Arif
Muhammad Affan Arif el 24 de Ag. de 2021
Thank you, very much. It solved my issue.
I apologize for late acknowledgement.

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