Fuzzy relational systems can represent symbolic knowledge in a formal numer
ical (subsymbolic) framework, with the aid of fuzzy relation equations. The
disadvantage of this methodology is the need for a priori knowledge in ord
er to construct the fuzzy relation equation. In this paper, a neural networ
k model is proposed in order to represent fuzzy relational systems without
the need of the construction of the fuzzy relation equation. The network en
sures the ideal perfect recall of fuzzy associative memories when the a pos
teriori constructed fuzzy relation equation has a non-empty solution set. I
t is actually a single layer of generalized neurons (compositional neurons)
that perform the sup-t-norm composition, An on-line learning algorithm ada
pting the weights of its interconnections is incorporated into the neural n
etwork. These weights are actually the elements of the fuzzy relation repre
senting the fuzzy relational system. The algorithm is based on the knowledg
e about the topographic structure of the respective fuzzy relation. (C) 200
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