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Modeling Uncertainty with Evolutionary Improved Fuzzy Functions

Fuzzy system modeling (FSM)– meaning the construction of a representation of
fuzzy systems models–is a difficult task. It demands an identification of many
parameters. This thesis analyses fuzzy-modeling problems and different approaches to
cope with it. It focuses on a novel evolutionary FSM approach–the design of “Improved
Fuzzy Functions” system models with the use of evolutionary algorithms. In order to
promote this analysis, local structures are identified with a new improved fuzzy
clustering method and represented with novel “fuzzy functions”.
The central contribution of this work is the use of evolutionary algorithms – in
particular, genetic algorithms– to find uncertainty interval of parameters to improve
“Fuzzy Function” models. To replace the standard fuzzy rule bases (FRBs) with the new
“Improved Fuzzy Functions” succeeds in capturing essential relationships in structure
identification processes and overcomes limitations exhibited by earlier FRB methods
because there are abundance of fuzzy operations and hence the difficulty of the choice of
amongst the t-norms and co-norms.
Designing an autonomous and robust FSM and reasoning with it is the prime goal of
this approach. This new FSM approach implements higher-level fuzzy sets to identify the
uncertainties in: (1) the system parameters, and (2) the structure of “Fuzzy Functions”.
With the identification of these parameters, an interval valued fuzzy sets and “Fuzzy
Functions” are identified. Finally, an evolutionary computing approach with the proposed
uncertainty identification strategy is combined to build FSMs that can automatically
identify these uncertainty intervals.
After testing proposed FSM tool on various benchmark problems, the algorithms are
successfully applied to model decision processes in two real problem domains:
desulphurization process in steel making and stock price prediction activities. For both
problems, the proposed methods produce robust and high performance models, which are
comparable (if not better) than the best system modeling approaches known in current
literature. Several aspects of the proposed methodologies are thoroughly analyzed to
provide a deeper understanding. These analyses show consistency of the results. / Full thesis submitted in paper.

Identiferoai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/11185
Date30 July 2008
CreatorsCelikyilmaz, Fethiye Asli
ContributorsTurksen, Ismail Burhan
Source SetsUniversity of Toronto
Languageen_ca
Detected LanguageEnglish
TypeThesis
Format34009 bytes, application/pdf

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