You're going to change the train function manifold, the train function is live in work, marquart back
propagation, we can change it to a different training function to know at least the training function
here.
It also mentioned and you can time help and train to see in a command window.
So if I click on that, let me show it to you here.
We have different type of training algorithm.
If you're writing a paper, you can usually try to apply different algorithm to your data and train
your artificial neural network.
And then in the end, you can just analyze the effect of each training function and training algorithm
on your system.
So let's check it out here.
We have a back propagation training function that to use Jacobean, which are the Bayesian regulation
back propagation and this L.M. that is set by default.
There are other back propagation, training function that's use gradient derivatives.
Here we have conjugate gradient back propagation.
We have gradient descent back propagation, which this one is a simple one.
The only one that we have here is gradient descent with adaptive L.R. back propagation and Vyatta momentum
so we can apply different training algorithm on our network and see the results.
So let me just choose one of them to show you how can we see the result?
I'm going to choose the gradient descent back propagation and applied on our network.
So this one is train g d gradient descent back to over Advanta scrape for changing this training.
I need to copy it first.
I just want to put it after this feting network, after I created my new fitting network here would
be good, just paste it and also add that that so instead of train Ilam, I'm going to choose train
gradient descent.
Now let's run the program to see if anything changed or not.
Yes, it changed.
Here we can see the training algorithm.
Now it's a gradient descent.
We have another factor in parameter, which was Mial.
Now we cannot see them anymore because it was for Laurinburg algorithm.
Let's see the feet, tools, how it was able to feed the network.
Not a good one.
In this example, the Lonberg algorithm or the propagation we choose a Jacob is best for this particular
network.
There are also some information you can read them.
For example, if you need to use less memory, then you can use train ACG and here we have Train R,
which takes longer but may be better for challenging problems where you just quickly reviewing each
of the algorithms.
There are also very good information in the help of Matlab.
You can choose the best that feeds your neural network.