AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

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HYBRID INTELLIGENT SYSTEMS
Figure 8.2
The neural knowledge base
certainty factor, can be associated with the weight between respective conjunction
and disjunction neurons (Fu, 1993; Kasabov, 1996). We will discuss specific
aspects of mapping rules into a neural network later, but now we shall return to
our example.
The neural knowledge base was trained with a set of training examples;
Figure 8.2 shows the actual numerical weights obtained between the first and the
second layers. If we now set each input of the input layer to either þ1 (true), 1
(false), or 0 (unknown), we can give a semantic interpretation for the activation
of any output neuron. For example, if the object has Wings ðþ1Þ, Beak ðþ1Þ and
Feathers ðþ1Þ, but does not have Engine ð 1Þ, then we can conclude that this
object is Bird ðþ1Þ:
X Rule1 ¼ 1 ð 0:8Þþ0 ð 0:2Þþ1 2:2 þ 1 2:8 þð 1Þð 1:1Þ
¼ 5:3 > 0;
Y Rule1 ¼ Y Bird ¼þ1:
We can similarly conclude that this object is not Plane,
X Rule2 ¼ 1 ð 0:7Þþ0 ð 0:1Þþ1 0:0 þ 1 ð 1:6Þþð 1Þ1:9
¼ 4:2 < 0;
Y Rule2 ¼ Y Plane ¼ 1:
and not Glider,
X Rule3 ¼ 1 ð 0:6Þþ0 ð 1:1Þþ1 ð 1:0Þþ1 ð 2:9Þþð 1Þð 1:3Þ
¼ 4:2 < 0;
Y Rule3 ¼ Y Glider ¼ 1: