Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems
Most applications of both type-1 and type-2 fuzzy logic systems are employing singleton fuzzification due to its simplicity and reduction in its computational speed. However, using singleton fuzzification assumes that the input data (i.e., measurements) are precise with no uncertainty associated wit...
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| Format: | Conference or Workshop Item |
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2016
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| Online Access: | https://eprints.nottingham.ac.uk/44630/ |
| _version_ | 1848796960863027200 |
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| author | Aladi, Jabran Hussain Wagner, Christian Pourabdollah, Amir |
| author_facet | Aladi, Jabran Hussain Wagner, Christian Pourabdollah, Amir |
| author_sort | Aladi, Jabran Hussain |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Most applications of both type-1 and type-2 fuzzy logic systems are employing singleton fuzzification due to its simplicity and reduction in its computational speed. However, using singleton fuzzification assumes that the input data (i.e., measurements) are precise with no uncertainty associated with them. This paper explores the potential of combining the uncertainty modelling capacity of interval type-2 fuzzy sets with the simplicity of type-1 fuzzy logic systems (FLSs) by using interval type-2 fuzzy sets solely as part of the non-singleton input fuzzifier. This paper builds on previous work and uses the methodological design of the footprint of uncertainty (FOU) of interval type-2 fuzzy sets for given levels of uncertainty. We provide a detailed investigation into the ability of both types of fuzzy sets (type-1 and interval type-2) to capture and model different levels of uncertainty/noise through varying the size of the FOU of the underlying input fuzzy sets from type-1 fuzzy sets to very “wide” interval type-2 fuzzy sets as part of type-1 non-singleton FLSs using interval type-2 input fuzzy sets. By applying the study in the context of chaotic time-series prediction, we show how, as uncertainty/noise increases, interval type-2 input fuzzy sets with FOUs of increasing size become more and more viable. |
| first_indexed | 2025-11-14T19:56:17Z |
| format | Conference or Workshop Item |
| id | nottingham-44630 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:56:17Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-446302020-05-04T17:59:55Z https://eprints.nottingham.ac.uk/44630/ Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems Aladi, Jabran Hussain Wagner, Christian Pourabdollah, Amir Most applications of both type-1 and type-2 fuzzy logic systems are employing singleton fuzzification due to its simplicity and reduction in its computational speed. However, using singleton fuzzification assumes that the input data (i.e., measurements) are precise with no uncertainty associated with them. This paper explores the potential of combining the uncertainty modelling capacity of interval type-2 fuzzy sets with the simplicity of type-1 fuzzy logic systems (FLSs) by using interval type-2 fuzzy sets solely as part of the non-singleton input fuzzifier. This paper builds on previous work and uses the methodological design of the footprint of uncertainty (FOU) of interval type-2 fuzzy sets for given levels of uncertainty. We provide a detailed investigation into the ability of both types of fuzzy sets (type-1 and interval type-2) to capture and model different levels of uncertainty/noise through varying the size of the FOU of the underlying input fuzzy sets from type-1 fuzzy sets to very “wide” interval type-2 fuzzy sets as part of type-1 non-singleton FLSs using interval type-2 input fuzzy sets. By applying the study in the context of chaotic time-series prediction, we show how, as uncertainty/noise increases, interval type-2 input fuzzy sets with FOUs of increasing size become more and more viable. 2016-07-24 Conference or Workshop Item PeerReviewed Aladi, Jabran Hussain, Wagner, Christian and Pourabdollah, Amir (2016) Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), 24-29 July 2016, Vancouver, Canada. Time series analysis Fuzzy logic Signal to noise ratio Noise measurement http://ieeexplore.ieee.org/document/7737943/ |
| spellingShingle | Time series analysis Fuzzy logic Signal to noise ratio Noise measurement Aladi, Jabran Hussain Wagner, Christian Pourabdollah, Amir Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title | Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title_full | Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title_fullStr | Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title_full_unstemmed | Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title_short | Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| title_sort | contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems |
| topic | Time series analysis Fuzzy logic Signal to noise ratio Noise measurement |
| url | https://eprints.nottingham.ac.uk/44630/ https://eprints.nottingham.ac.uk/44630/ |