Neural Networks Approach - Computational Intelligence in Time Series Forecasting - page 89

n

y ¦ wx ,

p ji i

i 1

where n is the number of input layer neurons connected with the activated neuron.
Using the set of weights learnt and stored, the network is capable of recognizing
the pattern once learnt and the patterns in its neighbourhoods because similar
inputs will activate the same Kohonen neuron.
After locating the Kohonen neuron, we turn to the Grossberg layer, i.e. the
output layer of the network, and train it. To produce the desired mapping of the
pattern at the network output using the output of the activated Kohonen neuron, all
we need is to connect this neuron with each neuron in the Grossberg layer using
the corresponding weights. As a result, a star connection between the Kohonen
neuron and the network output, known as Grossberg's outstar , builds the output
vector ( yy , ,..., y as shown in Figure 3.10. ),

1 p 2 p p

Outstar of Counter Propagation
network

y 1p

X 1 p

X 2 p y 2p

:
:

:
:

:
:

y mp

X np

Input Layer Output Layer

Kohonen Layer

Figure 3.10. Outstar of counterpropagation network

The input vectors of a counterpropagation network should generally be
normalized, i.e . they should satisfy the relation

x . 1

The normalization can be carried out by decreasing or increasing the vector length
to be on the unit sphere using the relation

x

x .

x

The question that remains is how to initialize the weight vectors before the network
training starts. The preference of taking the randomized weight vectors has not
always given reliable learning results. It has in some cases even created serious
solution problems. The way out was found in using the convex combination