
## Fig.1. Layered Feedforward Neural Network 3 Error Back Propagation Training Algorithm (Ebpta)

Here, Error Back Propagation Training algorithm and feedforward recall with one hidden layer has been used. In Fig. 1, Zi, Pj and Ok **are input, hidden and output** 
layer respectively. The weights between input and hidden layers are denoted by νji and the weights between hidden and output layers are denoted by Wkj**. The** 
procedure may easily be written down for the processing of this algorithm. Given R training pairs 
{Z1
, d1
;Z2
, d2
;.........ZR,dR}
where Zi (Ix1) are input and di **(Kx1) are desired values for the given inputs, the** 
error value is computed as E ( ) dk Ok
, k 2,1 **,.....**K
2 1 2
= − =
for the present neural network as shown in Fig. 1. 

The error signal terms of the output (δOk) and hidden layers (δPj**) are written** 
respectively as, Ok 5.0 (* dk Ok
)(1 Ok), k 2,1 **,........**K
2 δ = − − =
= − ∑ =
=
K
k Pj j Ok Pj P W j J
1 2 δ 5.0 1(* ) δ , 2,1 **,....**
Consequently, output layer weights (Wkj) and hidden layer weights (δji**) are** 
adjusted as, Z j, 2,1 .......J and i 2,1 **,.......I**
Pj i
(Old)
ji
(New)
ji ν =ν + βδ = =
W W P , k 2,1 .....K and j 2,1 **,.....J** Ok j
(Old)
kj
(New)
kj = + βδ = =
Where, β **is the learning constant.** 