Asked by aditi
on 22 Jan 2014

I have x and y coordinates and I want to fit an equation:

y=a*exp(x^b - 2^b)

to the data set and thus finding parameters a and b. Please help me through it.

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Answer by Amit
on 22 Jan 2014

Accepted Answer

First make a function that you'll use to fit like this:

function val = myfunc(par_fit,x,y) % par_fit = [a b]

val = norm(y - par_fit(1)*exp(x.^2-2^par_fit(2)));

Now, find the parameters like:

my_par = fminsearch(@(par_fit) myfunc(par_fit,x,y),rand(1,2));

Amit
on 23 Jan 2014

I did exactly what I told you earlier, just changed it to the new equation you mentioned.

I get values for a and b as, 5.55e7 and 7.12e7. Very Very high from what you said!!

I posted that plot because I wanted to show you that with limited number of data, you cannot estimate parameters for a very nonlinear function. You have to be very careful, especially in research, on how to determine parameters and then trust it.

aditi
on 23 Jan 2014

okay...i will follow previous instructions carefully...maybe i have done something wrong...

and a big thanks to u amit...u were of great help :) will contact u if m stuck again somewher else thanks

aditi
on 23 Jan 2014

one more thing...what i found after googling is that in such cases u have to give a specific range for 1 of the parameter... so any idea about that..??

like in above equation if i deliberately want that the b value should lie betweem 0.2 and 2 and then find a and b...how can i do that???

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Answer by Matt J
on 22 Jan 2014

You might also try FMINSPLEAS. It can take advantage of the fact that y has a linear dependence on one of the parameters 'a'.

Matt J
on 22 Jan 2014

aditi
on 22 Jan 2014

Matt J
on 22 Jan 2014

norm(y - par_fit(1)*exp(x.^2-2^par_fit(2)))

measures the distance between the vector y of given curve samples and the vector

par_fit(1)*exp(x.^2-2^par_fit(2))

of fitted curve samples.

fminsearch tries to find the par_fit(1) and par_fit(2) that minimizes this distance, giving best agreement between y and your parametric curve model.

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