I'm trying to optimize an output varying some coefficient. To do this I have an array (X) with different values. My output Y will be a function of X. I want to optimize Y varying X to have Y equal to an ideal one (Yideal). I'll give you some examples.
I have an array like:
X=[1 2 3 4 5 6]
My output is something like Y=f(X).
What I've done is something strange and works just partially.
I created a random vector with k=rand(length(X),1)*0.250+875; that will be multiplied by X.
Y will be slightly different.
Next, I will calculate the RMSE=sqrt((Y(Xc)-Yideal).^2) and, with a cycle, I search for the vector k that minimizes my RME.
The problem is that it works but it's very slow and it's too random. I'm asking if there is a way with Neural Network to find k vector that reduces RMSE.