Modeling uncertainty with evolutionary improved "fuzzy functions"

• Authors: Fethiye Asli Celikyilmaz
• Venue: Modeling uncertainty with evolutionary improved "fuzzy functions"
• Year: 2008

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.