curve fitting with fminsearch
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Burak Akayoglu
am 18 Mai 2020
Kommentiert: Burak Akayoglu
am 18 Mai 2020
Hello all,
I'm trying to learn MATLAB and take a course for that, and i have a homework that i can't solve. I have experimental datas for x and y variables(total 11 each) and the question asks me to fit the datas by using 'fminsearch' .
Can anyone help me how can i find the best curve and write a proper code? Thank you
x=[3, 5, 7, 10, 13, 17, 20, 23, 25, 29, 31];
y=[1.1 , 2.0 , 3.7 , 9.0 , 22.2 , 73.8 , 181.5,446.5 , 813.6 , 2701.3 , 4922.1];
2 Kommentare
Ameer Hamza
am 18 Mai 2020
Bearbeitet: Ameer Hamza
am 18 Mai 2020
Can you show what you have already tried? Even if you don't have a code, can you write down your understanding about solving this problem?
Akzeptierte Antwort
Ameer Hamza
am 18 Mai 2020
Study this example
x = [3, 5, 7, 10, 13, 17, 20, 23, 25, 29, 31];
y = [1.1 , 2.0 , 3.7 , 9.0 , 22.2 , 73.8 , 181.5,446.5 , 813.6 , 2701.3 , 4922.1];
f = @(a,b,x) a*exp(b*x);
obj_fun = @(params) norm(f(params(1), params(2), x)-y);
sol = fminsearch(obj_fun, rand(1,2));
a_sol = sol(1);
b_sol = sol(2);
figure;
plot(x, y, '+', 'MarkerSize', 10, 'LineWidth', 2)
hold on
plot(x, f(a_sol, b_sol, x), '-')
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/293923/image.png)
3 Kommentare
Ameer Hamza
am 18 Mai 2020
That a very useful observation. Many times differences in scale of the data points also make it difficult for optimizers to find an optimal solution. Such modifications can make things easy for the optimizer.
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Ang Feng
am 18 Mai 2020
Hi Burak,
This link is certainly helpful
Matlab has very good documentation.
Good luck
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