How to resolve : increase max function value in fitting using fminsearch?

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Somnath Kale
Somnath Kale 2022 年 6 月 16 日
編集済み: Matt J 2022 年 6 月 18 日
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!


Matt J
Matt J 2022 年 6 月 16 日
編集済み: Matt J 2022 年 6 月 16 日
You could do as the message says and increas MaxFunEvals, but for your model, it would be better to download fminspleas,
funlist={1,@(c,xd) -xd(:).^c};
[c,ab]=fminspleas(funlist, 1 ,x, y);
sol = 1×3
-6.5546 -0.0000 -6.0133
  2 件のコメント
Matt J
Matt J 2022 年 6 月 17 日
Fminspleas uses a technique which only needs to iterate over the c parameter, so it is an easier search.


その他の回答 (1 件)

Matt J
Matt J 2022 年 6 月 16 日
編集済み: Matt J 2022 年 6 月 16 日
If you have the Curve Fitting Toolbox,
ft =
General model Power2: ft(x) = a*x^b+c Coefficients (with 95% confidence bounds): a = 1.124e-06 (-2.414e-05, 2.639e-05) b = -6.015 (-14.64, 2.609) c = -6.554 (-9.987, -3.121)
  5 件のコメント
Somnath Kale
Somnath Kale 2022 年 6 月 17 日
@Matt J Im more intrested in fitting coefficint than that the good visual fit. I tried with fminplease it doing the job!
Thanks! looking forword to your help in future as well!


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