Modular rule base fuzzy networks for linguistic composition based modelling

Alexander Gegov, Nedyalko Petrov, David Sanders, Boriana Vatchova

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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.
Original languageEnglish
Pages (from-to)53-67
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Issue number2
Publication statusPublished - 23 Feb 2017


  • fuzzy system models
  • decision analysis
  • large-scale systems
  • linguistic modelling


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