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A fuzzy system modeling algorithm for data analysis and approximate reasoning |
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| Authors: | Kemal Beth A. I. Burhan Claudio A. |
| Affiliation: | aFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey bDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada cCentre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada dPsychopharmacology Research Program, Sunnybrook and Women's College Health Sciences Centre, University of Toronto, Toronto, ON, Canada eFaculty of Pharmacy, University of Toronto, Toronto, ON, Canada fDepartments of Psychiatry, University of Toronto, Toronto, ON, Canada gDepartments of Pharmacology, University of Toronto, Toronto, ON, Canada hDepartments of Medicine, University of Toronto, Toronto, ON, Canada |
| Abstract: | In this paper a new fuzzy system modeling (FSM) algorithm is introduced as a data analysis and approximate reasoning tool. The performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison two benchmark data sets from the literature, namely the automobile mpg (miles per gallon) prediction and Box and Jenkins gas-furnace data are used. The comparisons demonstrated that the proposed algorithm can be successfully applied in system modeling. |
| Keywords: | Fuzzy system Data analysis Approximate reasoning |
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