Geometric Fuzzy Logic Systems
| dc.contributor.author | Coupland, Simon | |
| dc.date.accessioned | 2015-03-16T15:50:24Z | |
| dc.date.available | 2015-03-16T15:50:24Z | |
| dc.date.issued | 2006 | |
| dc.description.abstract | There has recently been a significant increase in academic interest in the field oftype-2 fuzzy sets and systems. Type-2 fuzzy systems offer the ability to model and reason with uncertain concepts. When faced with uncertainties type-2 fuzzy systems should, theoretically, give an increase in performance over type-l fuzzy systems. However, the computational complexity of generalised type-2 fuzzy systems is significantly higher than type-l systems. A direct consequence of this is that, prior to this thesis, generalised type-2 fuzzy logic has not yet been applied in a time critical domain, such as control. Control applications are the main application area of type-l fuzzy systems with the literature reporting many successes in this area. Clearly the computational complexity oftype-2 fuzzy logic is holding the field back. This restriction on the development oftype-2 fuzzy systems is tackled in this research. This thesis presents the novel approach ofdefining fuzzy sets as geometric objects - geometric fuzzy sets. The logical operations for geometric fuzzy sets are defined as geometric manipulations of these sets. This novel geometric approach is applied to type-I, type-2 interval and generalised type-2 fuzzy sets and systems. The major contribution of this research is the reduction in the computational complexity oftype-2 fuzzy logic that results from the application of the geometric approach. This reduction in computational complexity is so substantial that generalised type-2 fuzzy logic has, for the first time, been successfully applied to a control problem - mobile robot navigation. A detailed comparison between the performance of the generalised type-2 fuzzy controller and the performance of the type-l and type-2 interval controllers is given. The results indicate that the generalised type-2 fuzzy logic controller outperforms the other robot controllers. This outcome suggests that generalised type-2 fuzzy systems can offer an improved performance over type-l and type-2 interval systems. | en |
| dc.identifier.uri | http://hdl.handle.net/2086/10782 | |
| dc.language.iso | en | en |
| dc.publisher | De Montfort University | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.title | Geometric Fuzzy Logic Systems | en |
| dc.type | Thesis or dissertation | en |
| dc.type.qualificationlevel | Doctoral | en |
| dc.type.qualificationname | PhD | en |
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