Find coefficient in order to match two functions with Neural Network

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.

Xc=X.*k';

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.

Answer 1

[Sahil Jain]

The problem you have described is known as linear regression. You can go through the Linear Regression documentation page to learn more about how to solve such problems in MATLAB.

[Marco Pellegrino]

Okay, i'll try it. Thx a lot

[Marco Pellegrino]

The problem is that the Y vector has not the same number of element of X vector.