how to use fitnlm with constraints

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Haneya Qureshi
Haneya Qureshi 2023-3-11
评论： Haneya Qureshi 2023-3-12
I have a custom equation and want to fit its coeffients.
coefficients are k
x is data
Equation is as follows:
modelfun = @(k,x) k(1).*x(:,1)+k(2).*log10(x(:,2) +k(3).*x(:,3)^2)
Kinit is vector of initial variables that I give
tbl contains my data x
I fit like this:
mdl = fitnlm(tbl,modelfun,Kinit);
I want to impose certain contraints on coefficients,
i.e.,
k(1) should be between 20 and 40
k(2) should be positive
k(3) should be negative.
How can I do that?
Thanks a lot for helping me out.
  2 个评论
Walter Roberson
Walter Roberson 2023-3-11
log10*x(:,2)
could you confirm that you assigned a value to log10 such that you can multiply it by an input? Or did you miss some () ?
Haneya Qureshi
Haneya Qureshi 2023-3-11
编辑：Haneya Qureshi 2023-3-11
@Walter Roberson sorry, i missed brackets..the function is definitely non-linear.
modelfun = @(k,x) k(1).*x(:,1)+k(2).*log10(x(:,2) +k(3).*x(:,3)^2)

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Walter Roberson
Walter Roberson 2023-3-11
No, fitnlm does not provide any way to put in constraitns.
You might consider lsqcurvefit from the Optimization Toolbox. Or you could consider using the Curve Fitting Toolbox; https://www.mathworks.com/help/curvefit/linear-and-nonlinear-regression.html
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Walter Roberson
Walter Roberson 2023-3-11
Ah, yes. Curve Fitting Toolbox can support functions of two variables, creating a surface fit, but that is not enough for your purposes.
You could consider creating a residue function,
residue = @(k) sum((k(1) * x(:,1) + k(2) * log10(x(:,2)) + k(3)*x(:,3).^2) - Y).^2)
and minimizing that sum-of-squares using fmincon.

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