The Development of Neural Network Based System Identification ...

4.2 THE ARTIFICIAL NEURAL NETWORKS 81
X 1
W h,1
X 2
W h,2
f
h
X 3
W h,3
x h
yˆ h
W h,4
X 4
W hm ,
X m
+1
b 1
Bias Input
(a)
1
2
Total
Weighted
Input
Neuron Unit
Output
0.5
SUM
ACTIVATION
FUNCTION
0.1
Input Weight
Weighted
Input
(b)
Figure 4.2
The artificial neuron model with multiple inputs and single output: (a) The output is
represented in compact mathematical form as ŷ h = f h
( ∑m
j=1 W hjX j + b 1
). The term h represents the
number of neurons in the network and m is the number of inputs entering the neuron; and (b) The
detailed working mechanism of a single neuron. Figure adapted from Samarasinghe [2007].
classification schemes. For system identification and regression modelling problems, it is
common to use the hyperbolic tangent function in the hidden layer and linear function
for output layer.
4.2.1 Multi-Layered Perceptron
One of the most popularly used neural network architectures is based on feed-forward
Multi-Layered Perceptron (MLP). Multilayer Perceptron (MLP) is a class of ANN
which is built from several layers of neurons which only allows unidirectional signal