Context dependent fuzzy modelling and its applications
Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of th...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2013
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| Online Access: | https://eprints.nottingham.ac.uk/13574/ |
| _version_ | 1848791764459061248 |
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| author | Ho, Duc Thang |
| author_facet | Ho, Duc Thang |
| author_sort | Ho, Duc Thang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature. |
| first_indexed | 2025-11-14T18:33:42Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-13574 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:33:42Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-135742025-02-28T11:25:58Z https://eprints.nottingham.ac.uk/13574/ Context dependent fuzzy modelling and its applications Ho, Duc Thang Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature. 2013-12-15 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/13574/1/thesis_with_pdfcode.pdf Ho, Duc Thang (2013) Context dependent fuzzy modelling and its applications. PhD thesis, University of Nottingham. fuzzy modelling fuzzy logic applications computing computer games video games car racing |
| spellingShingle | fuzzy modelling fuzzy logic applications computing computer games video games car racing Ho, Duc Thang Context dependent fuzzy modelling and its applications |
| title | Context dependent fuzzy modelling and its applications |
| title_full | Context dependent fuzzy modelling and its applications |
| title_fullStr | Context dependent fuzzy modelling and its applications |
| title_full_unstemmed | Context dependent fuzzy modelling and its applications |
| title_short | Context dependent fuzzy modelling and its applications |
| title_sort | context dependent fuzzy modelling and its applications |
| topic | fuzzy modelling fuzzy logic applications computing computer games video games car racing |
| url | https://eprints.nottingham.ac.uk/13574/ |