Affiliations: [a] School of Computing Buckingham Building, University of Portsmouth, Portsmouth PO1 3HE, UK | [b] School of Engineering Anglesea Building, University of Portsmouth, Portsmouth PO1 3DJ, UK | [c] Institute of Information and Communication Technologies Block 2, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Correspondence:
[*]
Corresponding author: Alexander Gegov, School of Computing Buckingham Building, University of Portsmouth, Portsmouth PO1 3HE, UK. E-mail:alexander.gegov@port.ac.uk
Abstract: This paper proposes a linguistic composition based
modelling approach by networked fuzzy systems that are known as fuzzy
networks. The nodes in these networks are modules of fuzzy rule bases and
the connections between these modules are the outputs from some rule bases
that are fed as inputs to other rule bases. The proposed approach represents
a fuzzy network as an equivalent fuzzy system by linguistic composition of
the network nodes. In comparison to the known multiple rule base approaches,
this networked rule base approach reflects adequately the structure of the
modelled process in terms of interacting sub-processes and leads to more
accurate solutions. The approach improves significantly the transparency of
the associated model while ensuring a high level of accuracy. Another
advantage of this fuzzy network approach is that it fits well within the
existing approaches with single rule base and multiple rule bases.
Keywords: Fuzzy system models, decision analysis, large-scale systems, linguistic modelling
