Advances in fuzzy systems and networks

Author

Gegov, Alexander

Author_Institution

Sch. of Comput., Univ. of Portsmouth, Portsmouth, UK

fYear

2012

fDate

6-8 Sept. 2012

Firstpage

33

Lastpage

40

Abstract

This plenary paper describes two novel fuzzy modelling methodologies for complex processes that are characterised by uncertainty, dimensionality and structure. The first methodology is based on rule base compression in fuzzy systems whereby model efficiency is improved without loss of model accuracy. The second methodology is based on fuzzy networks with modular rule bases whereby model transparency is improved at minimal loss of model accuracy. The two methodologies are validated comparatively on several case studies. The comparison shows the overall superiority of these methodologies to the current methodologies of rule base reduction in fuzzy systems and fuzzy networks with chained rule bases.

Keywords

complex networks; fuzzy logic; fuzzy systems; knowledge based systems; uncertainty handling; chained rule base; complex networks; complex systems; dimensionality characteristic; fuzzy networks; fuzzy systems; model transparency improvement; modular rule base; rule base compression; rule base reduction; structural characteristic; uncertainty characteristic; Accuracy; Complexity theory; Computational modeling; Fuzzy systems; Knowledge based systems; Pragmatics; Uncertainty; complex networks; complex systems; fuzzy networks; fuzzy systems; rule base compression; rule base reductioon; rule based networks; rule based systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335111

Filename

6335111