Interval type-2 A-intuitionistic fuzzy logic for regression problems
This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorithm is used to adapt the parameters of the IT2AIFLS...
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| Format: | Article |
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IEEE
2017
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| Online Access: | https://eprints.nottingham.ac.uk/48037/ |
| _version_ | 1848797677366542336 |
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| author | Eyoh, Imo John, Robert de Maere, Geert |
| author_facet | Eyoh, Imo John, Robert de Maere, Geert |
| author_sort | Eyoh, Imo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorithm is used to adapt the parameters of the IT2AIFLS. The empirical comparison is made on the designed system using some benchmark regression problems - both artificial and real world datasets. Analyses of our results reveal that IT2AIFLS outperforms its type-1 variant, other type-1 fuzzy logic approaches and some type-2 fuzzy systems in the regression tasks. The reason for the improved performance of the proposed framework of IT2AIFLS is because of the introduction of non-membership functions and intuitionistic fuzzy indices into the classical IT2FLS model. This increases the level of fuzziness in the proposed IT2AIFLS framework, thus providing more accurate approximations than AIFLS, classical type-1 and interval type-2 fuzzy logic systems. |
| first_indexed | 2025-11-14T20:07:41Z |
| format | Article |
| id | nottingham-48037 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:07:41Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-480372020-05-04T19:18:53Z https://eprints.nottingham.ac.uk/48037/ Interval type-2 A-intuitionistic fuzzy logic for regression problems Eyoh, Imo John, Robert de Maere, Geert This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorithm is used to adapt the parameters of the IT2AIFLS. The empirical comparison is made on the designed system using some benchmark regression problems - both artificial and real world datasets. Analyses of our results reveal that IT2AIFLS outperforms its type-1 variant, other type-1 fuzzy logic approaches and some type-2 fuzzy systems in the regression tasks. The reason for the improved performance of the proposed framework of IT2AIFLS is because of the introduction of non-membership functions and intuitionistic fuzzy indices into the classical IT2FLS model. This increases the level of fuzziness in the proposed IT2AIFLS framework, thus providing more accurate approximations than AIFLS, classical type-1 and interval type-2 fuzzy logic systems. IEEE 2017-11-20 Article PeerReviewed Eyoh, Imo, John, Robert and de Maere, Geert (2017) Interval type-2 A-intuitionistic fuzzy logic for regression problems. IEEE Transactions on Fuzzy Systems . ISSN 1941-0034 Interval type-2 A-intuitionistic fuzzy logic system; Regression problems; Gradient descent algorithm http://ieeexplore.ieee.org/document/8115302/ doi:10.1109/TFUZZ.2017.2775599 doi:10.1109/TFUZZ.2017.2775599 |
| spellingShingle | Interval type-2 A-intuitionistic fuzzy logic system; Regression problems; Gradient descent algorithm Eyoh, Imo John, Robert de Maere, Geert Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title | Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title_full | Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title_fullStr | Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title_full_unstemmed | Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title_short | Interval type-2 A-intuitionistic fuzzy logic for regression problems |
| title_sort | interval type-2 a-intuitionistic fuzzy logic for regression problems |
| topic | Interval type-2 A-intuitionistic fuzzy logic system; Regression problems; Gradient descent algorithm |
| url | https://eprints.nottingham.ac.uk/48037/ https://eprints.nottingham.ac.uk/48037/ https://eprints.nottingham.ac.uk/48037/ |