How to resolve : increase max function value in fitting using fminsearch?
显示 更早的评论
Hi
I was trying to fit my data with fminsearch function with following code:
f = @(a,b,c,x) a - b.*(x).^c;
obj_fun = @(params) norm(f(params(1), params(2), params(3), x) -y);
sol = fminsearch(obj_fun, [1,1,1]);
err = .02*ones(size(x));
errorbar(x,y,err,'horizontal','s',"MarkerFaceColor",[0.8500, 0.3250, 0.0980], ...
"MarkerSize",4,"CapSize",4,"Color",[0.8500, 0.3250, 0.0980],"LineWidth",1)
hold on
x = linspace(min,max,20);
plot(x,f(sol(1),sol(2),sol(3),x),'-',"Color",[0.8500, 0.3250, 0.0980],"LineWidth",1)
hold off
Its getting the fit, but I think this is not best optimum fit its showing following message:
Exiting: Maximum number of function evaluations has been exceeded
- increase MaxFunEvals option.
Current function value: 2.586758
it will be realy great if some experties help me here to take care of this. Im attaching data here (data.txt).
Is there any other function which I can use instade of this to fit and better gobal optimazation.
Thank you in advance!
采纳的回答
You could do as the message says and increas MaxFunEvals, but for your model, it would be better to download fminspleas,
[x,y]=readvars('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1034515/data.txt');
funlist={1,@(c,xd) -xd(:).^c};
[c,ab]=fminspleas(funlist, 1 ,x, y);
sol=[ab(:).',c]
更多回答（1 个）
If you have the Curve Fitting Toolbox,
[x,y]=readvars('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1034515/data.txt');
ft=fit(x(:),y(:),'power2')
plot(ft,x,y)
5 个评论
Alex Sha
2022-6-17
adding one term "d*x" for fitting function, the result will be much better:
y = a*x^b+c+d*x
Sum Squared Error (SSE): 1.0988630107163
Root of Mean Square Error (RMSE): 0.3494222232194
Correlation Coef. (R): 0.970379925110827
R-Square: 0.941637199058095
Parameter Best Estimate Std. Deviation Confidence Bounds[95%]
--------- ------------- -------------- ----------------------------------
a 430058778433358 0.125126556164325 [430058778433358, 430058778433359]
b 14.4836582099507 3.10784213390318 [6.49469567251403, 22.4726207473875]
c 18.5003218509943 36.6945561128952 [-75.8260375595516, 112.82668126154]
d -257.754044491992 1.5875823760405E-17 [-257.754044491992, -257.754044491992]
Alex Sha
2022-6-17
or the function: y=a*x^b+c+d*exp(e*x);
Sum Squared Error (SSE): 0.798012086224085
Root of Mean Square Error (RMSE): 0.297771740735171
Correlation Coef. (R): 0.978578556003836
R-Square: 0.957615990270552
Parameter Best Estimate Std. Deviation Confidence Interval[95%] (Diff-OK)
--------- ------------- -------------- ----------------------------------
a -3.96448222151296E-7 7.34052972781328E-6 [-2.07770260544974E-5, 1.99841296101948E-5]
b -7.72186291539508 6.95514357122107 [-27.0324372396597, 11.5887114088695]
c -1.28127707754111 12.6536549323532 [-36.4134553773351, 33.8509012222529]
d 286678.994342762 1537623.25203099 [-3982447.55739699, 4555805.54608251]
e -97.6137053829133 77.3303025621153 [-312.317045414963, 117.089634649136]
Somnath Kale
2022-6-17
Matt J
2022-6-17
@Sonnath what is unacceptable about the fit that your current model gives you? You'll notice that both fit() and fminspleas() are in agreement on the fitted parameters.
Somnath Kale
2022-6-17
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