Hi!
I have a question, I would like to make a simulation model through reverse modelling. I have a data set to fit the simulation model. I tried to write a piece of code to practise, but I dont know how to make it work. What I want to do is making some kind of code (already got it) with for example three unknown parameters. These three parameters should be 'fit' within a range so that the mean squared error is as close to 0 as possible. I guess I need a function where after I try to find the combination of parameters which will result in mean squared error of around 0.
I don't need someone to do my homework, but I like to know were to start and what I need to google.
This is what I got so far:
function [m] = stat(a,b,c,d)
e = sin(a);
f = cos(b);
g = tan(c);
h = e*f;
i = g+e;
s = h-i;
m = sum(sqrt((s-d).^2));
end
and the code:
dataset = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21];
dataset = sin(dataset);
for p1 = 0 : 1 : 21
for p2 = 0.0 :0.1:2.0
for p3 = 0.01 :0.01: 0.4
[answer] = stat(p1,p2,p3,dataset);
disp(answer)
end
end
end
p1 = 0 : 1 : 21;
p2 = 0.0 :0.1:2.0;
p3 = 0.01 :0.01: 0.4;
fit = fminsearch(stat,0);
disp(fit)
Thank you!
