fitting an equation to the curve
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I have set of data X,Y and i would like to fit an equation of the form
.
I want to find for which n, the curev fits the best.
Any help!
9 comentarios
Rik
el 15 de Nov. de 2018
You really shouldn't shadow the error function with a variable. Once you rename it, this code should work. I tried implementing fminsearch, but it is difficult to find a good way to select only discrete results.
Respuestas (3)
KSSV
el 15 de Nov. de 2018
Read about polyfit
N = 100 ; n = 2 ;
x = linspace(0,2*pi,N) ;
y = sin(x).^n ;
%
p = polyfit(x,y,7); % you can change 7 here...
x1 = linspace(0,2*pi);
y1 = polyval(p,x1);
figure
plot(x,y,'o')
hold on
plot(x1,y1)
hold off
5 comentarios
madhan ravi
el 15 de Nov. de 2018
Editada: madhan ravi
el 15 de Nov. de 2018
x = linspace(-2*pi,2*pi,100) ;
xx=linspace(x(1),x(end),1000);
y = sin(x).^7 ;
yy=interp1(x,y,x,'spline')
figure
plot(x,y,'o',x,yy,'r')
1 comentario
Image Analyst
el 15 de Nov. de 2018
My favorite fitting function is fitnlm() in the Statistics and Machine Learning toolbox. It could easily find this n. Just attach your data in a .mat file and I can do it for you. You have a non-linear equation in variable n. You could turn it into a linear equation in n by taking the log of the data and then using polyfit, but do you know how that affects the accuracy? Why not use fitnlm() to fit a non-linear model to n right from the start?
4 comentarios
Rik
el 15 de Nov. de 2018
Ah, yes, you are right. But as I already mentioned, it doesn't matter. The exponent should be an integer, so this will be a real value for any valid n.
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