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Response surface method in Matlab
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Arash
el 2 de Mzo. de 2020
Respondida: Thonn Homsnit
el 10 de Mayo de 2022
Hi.
I have 6 independent variable and one dependent variable. I want to fit a response surface to the data by for exp a second order polynomial, when I open curve fitting toolbox, There is only two space for the input variable. can anyone help me about this. How can I do this fit in matlab?
Thank you in advance for your time.
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Thonn Homsnit
el 10 de Mayo de 2022
I faced the same problem as the author. But I have just figured it out !
I use the 'fitnlm' function with the user defined function with handle like this
modelFun = @(b1,x1)( ...
+b1(1).*x1(:,1) ...
+b1(2).*x1(:,2) ...
+b1(3).*x1(:,3)...
+b1(4).*x1(:,4)...
+b1(5).*x1(:,1).*x1(:,2) ...
+b1(6).*x1(:,1).*x1(:,3) ...
+b1(7).*x1(:,1).*x1(:,4) ...
+b1(8).*x1(:,2).*x1(:,3) ...
+b1(9).*x1(:,2).*x1(:,4) ...
+b1(10).*x1(:,3).*x1(:,4) ...
+b1(11).*x1(:,1).*x1(:,1) ...
+b1(12).*x1(:,2).*x1(:,2) ...
+b1(13).*x1(:,3).*x1(:,3) ...
+b1(14).*x1(:,4).*x1(:,4) ...
+b1(15) ...
);
beta0 = zeros(15,1); %%% initial coefficient value %%%%
mdl1 = fitnlm(x1,y1,modelFun,beta0);
b1 = table2array(mdl1.Coefficients(:,"Estimate"));
x1 is predictor matrix and y1 is target vector. you can solve the b1 matrix which is the coefficient of this function. Another tool is using MBC toolbox. But the documentation is very old and i cannot find the algorithm behind the toolbox.
Hope it helps.
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