Suppose we have some samples, issues of our independent variable X and this one is over dependent variable
Y.
And here are our samples.
So I'm looking for a way to feed a function to satisfy and to cover all those data.
Here I can already try.
OK, not a very good fit, but however, as you can see, this is a parabolic curve for a parabolic
curve.
We can already write a function not very accurate, but here is a zero plus a one X plus a two.
And X Square, we are looking for a tool to give us the best fit today's samples, we are looking for
minimizing the error, the distance between these simple and this curve, it supposedly must be on the
curve.
But however, we cannot just draw a line like that to satisfy all the samples.
We have to just draw a curve to satisfy most of the samples, but we really can't do it for all.
Let me just raise it again.
Let's just try to fit another parabolic curve.
I'm going to say this one maybe.
So we want to use artificial neural network to help us feed this curve.
Now let's check out the error that we can have here, the distance between these two here.
I can call this one E of eye disease.
Or if I and if you just started here, this is our data and this is just what we were able to fit.
This is over ViiV I and this one is white hat to I, the one which we were able to fit of.
Our task here is to minimize the error by using the meniscus or they're looking to minimizing these
errors.
Here we have another one.
This is another EFI and we are looking to minimize these errors and fit the best curve to satisfy all
the data in the technical section.
We all did it.
Learn how to calculate the mean square error and Verity's formula is coming from now.
Let's take a look at this point.
Our goal is to just say that line should be equal to we have.
This is the best situation, which I already explained, that it's not going to happen.
The error cannot be zero because of several factors, especially with the natural systems, both of
them, we are looking to carve a parabolic curve.
So here is a zero plus a one X and a two X square.
The factors that we can change in order to fit the best curve are a zero A1 A2.
So let's find out the best value that we can set for these variables in order to have the best foetid
curve to start working with Matlab tools, we need to have some data.
So let's generate a data X.
If I am going to say it's between zero to two pi y if I equals two signers of X of mine.
This is just one sample to see how the neural network is going to work.
And of course I is going from one to and in the other hand we have over process, so we are feting X,
which is over input to two systems.
One is of a process which is a sign of X and the other one is artificial neural network.
So our goal is to train this network by showing it several samples and we want to have the same output,
or at least we are trying to minimize the error for these two outputs.
Now let's back to Matlab and generate these samples.