Type-2 Fuzzy Probabilistic System for Proactive Monitoring of Uncertain Data-intensive Seasonal Time Series
| dc.contributor.author | Wang, Yuying | |
| dc.date.accessioned | 2015-07-06T15:29:20Z | |
| dc.date.available | 2015-07-06T15:29:20Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | This research realises a type-2 fuzzy probabilistic system for proactive monitoring of uncertain data-intensive seasonal time series in both theoretical and practical implications. In this thesis, a new form of representation, J˜-plane, is proposed for concave and unnormalized type-2 fuzzy events as well as convex and normalized ones, which facilitates bridging the gaps between higher order fuzzy probability realizations and real world problems. Since J˜-plane representation, the investigation of type-2 fuzzy probability theory and the proposal of a type-2 fuzzy probabilistic system become possible. Based on J˜-plane representation, a new fuzzy systemmodel - a type-2 fuzzy probabilistic system is proposed incorporating probabilistic inference with type-2 fuzzy sets. A special case study, a type-2 fuzzy SARIMA system is proposed and experimented in forecasting singleton and uncertain non-singleton bench mark data - Mackey-Glass time series. The results show that the type-2 fuzzy SARIMA system has achieved significant improvements beyond its predecessors - the classical statistical model - SARIMA, type-1 and general type-2 fuzzy logic systems, no matter whether in the singleton or the non-singleton experiments, whereas a SARIMA model cannot forecast non-singleton data at all. The type-2 fuzzy SARIMA system is applied in a real world scenario - WSS CAPS proactive monitoring, and compared with the results of the statistical model SARIMA, type-1 and general type-2 fuzzy logic systems to show that, the type-2 fuzzy SARIMA system can monitor practical uncertain data-intensive seasonal time series proactively and accurately, whereas its predecessors - the statistical model SARIMA, type-1 and general type-2 fuzzy logic systems - cannot deal with this at all. As a series of concepts, algorithms, experiments, practical implements and comparisons prove that, a type-2 fuzzy probabilistic system is viable in practice which realises that type-2 fuzzy systems evolve from rule-based fuzzy systems to the systems incorporating probabilistic inference with type-2 fuzzy sets. | en |
| dc.identifier.uri | http://hdl.handle.net/2086/11059 | |
| dc.language.iso | en | en |
| dc.publisher | De Montfort University | en |
| dc.publisher.department | Faculty of Technology | en |
| dc.publisher.department | Centre for Computational Intelligence | en |
| dc.rights.embargodate | 2016-09-26 | |
| dc.subject | Type-2 fuzzy probabilistic system | en |
| dc.subject | Type-2 fuzz probability theory | en |
| dc.title | Type-2 Fuzzy Probabilistic System for Proactive Monitoring of Uncertain Data-intensive Seasonal Time Series | en |
| dc.type | Thesis or dissertation | en |
| dc.type.qualificationlevel | Doctoral | en |
| dc.type.qualificationname | PhD | en |
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