Digital Signal Processing Reference
In-Depth Information
Like the other neural networks, the Hopfield network has the follow-
ing four components:
Neurons: The Hopfield network has a finite set of neurons x ( i ) , 1
i
N which serve as processing units. Each neuron has a value (or
state) at time t , described by x t ( i ). A neuron in the Hopfield network
has one of the two states, either -1 or +1; that is, x t ( i )
.
Synaptic connections: The learned information of a neural net-
work resides within the interconnections between its neurons. For each
pair of neurons x ( i )and x ( j ), there is a connection w ij , called the
synapse, between them. The design of the Hopfield network requires that
w ij = w ji and w ii = 0. Figure 6.13a illustrates a three-node network.
Propagation rule: It defines how states and synapses influence the
input of a neuron. The propagation rule τ t ( i )isdefinedby
∈{−
1 , +1
}
N
τ t ( i )=
x t ( j ) w ij + b i
(6.40)
j=1
b i is the externally applied bias to the neuron.
Activation function: The activation function f determines the
next state of the neuron x t+1 ( i ) based on the value τ t ( i )computedby
the propagation rule and the current value x t ( i ). Figure 6.13b illustrates
this. The activation function for the Hopfield network, is the hard limiter
defined here:
x t+1 ( i )= f ( τ t ( i ) , x t ( i )) = 1 ,
if
τ t ( i ) > 0
(6.41)
1 ,
if
τ t ( i ) < 0
The network learns patterns that are N -dimensional vectors from the
space P =
N .Let e k =[ e k
1
,e k
2
,...,e n ]definethe k th exemplar
{−
1 , 1
}
pattern where 1
K . The dimensionality of the pattern space is
reflected in the number of nodes in the network, such that the latter will
have N nodes x (1) , x (2) ,..., x ( N ).
k
The training algorithm of the Hopfield neural network is simple and
outlined below.
1. Learning: Assign weights w ij
to the synaptic connections:
w ij = k=1 e i e j ,
if
i
= j
(6.42)
0 ,
if
i = j
Keep in mind that w ij = w ji , so it is necessary to perform the preceding
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