Procedure FFNN:

Input Data:

· x(t) is a time series of k observations

· y(t) input-output mapping value

Define Hyper-Parameters:

· Epoch

· Learning rate (η)

· Batch Size

· Class weight

· Callbacks

· Dense layer

· Dropout layer

Initialize Weight

· w_i: Initial input weight to random value

Define the Cost Function:

· Same as in Equation (9)

For i in Range(Epoch):

· Calculate E for all inputs. If E is smaller than tolerated value then exit from the loop with raising an exception

· For each input, calculate gradients for all weights, included bias weights

· If length of gradient vector is smaller than given minimum border value, then raise an exception and exit from the loop.

· Else modify all weights by adding a negative multiple of the gradient to the weights, calculate accuracy and loss for the given epoch.

End if

End loop

End Procedure