Abstract
We introduce and study a new concept of fuzzy computing units.
This construct is is aimed at coping with "negative" (inhibitory)
information and accommodating it in the language of fuzzy sets. The
essential concept developed in this study deals with computing units
exploiting the concept of balanced fuzzy sets. We recall how the
membership notion of fuzzy sets can be extended to the [-1,1]
range giving rise to balanced fuzzy sets and then summarize properties
of augmented (extended) logic operations for these constructs. We show
that this idea is particularly appealing in neurocomputing as the
"negative" information captured through balanced fuzzy sets exhibits a
straightforward correspondence with inhibitory processing mechanisms
encountered in neural networks. This gives rise to interesting properties
of balanced computing units when compared with fuzzy and logic neurons
developed on the basis of classical logic and classical fuzzy sets.
Illustrative examples concerning topologies and properties and learning
of balanced fuzzy computing units are included. A number of illustrative
examples concerning topologies, properties and learning of balanced
fuzzy fuzzy computing units are included.