Fuzzy modeling using genetic algorithms | |
| Author | Sengo, Veluppillai |
| Call Number | AIT Thesis no.CS-94-8 |
| Subject(s) | Genetic algorithms Fuzzy arithmetic |
| Note | A thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | Fuzzy modeling has become a potential research area in recent years. Lots of attempts have been made recently to identify fuzzy models from input output data. Analyzing through all available methods, it is found that the fuzzy c-means clustering based fuzzy modeling method is very impressive even though it needs some modifications. In this research some new ideas are introduced to improve the above mentioned fuzzy modeling approach. The areas in which modifications required are analyzed and new ideas proposed. Genetic algorithms are used in various tuning processes to improve the performance of identified fuzzy models. The proposed method is fully implemented and tested with a chemical plant control, a nonlinear system and a stock market modeling problems. These three different kinds of case studies are considered in order to illustrate the applicability of the approach to diversified fields. Finally, effectiveness of the proposed modeling approach is demonstrated by comparing with existing modeling approach. |
| Year | 1994 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Other Field of Studies (No Department) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Vilas Wuwongse |
| Examination Committee(s) | Huynh Ngoc Phien ;Sadananda, Ramakoti |
| Scholarship Donor(s) | Government of Finland |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1994 |