Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and nor...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | English |
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Springer
2017
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| Online Access: | http://ir.unimas.my/id/eprint/15755/ http://ir.unimas.my/id/eprint/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf |
| _version_ | 1848837919688622080 |
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| author | Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng |
| author_facet | Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng |
| author_sort | Teh, Chin Ying |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and normal trapezoid fuzzy sets are used as fuzzy membership functions. Besides that, a strong fuzzy partition strategy is used. Our empirical analysis shows that a set of multi-attribute monotone data may lead to non-monotone fuzzy If-Then rules. The same observation can be made, empirically, using adaptive neuro-fuzzy inference system (ANFIS), a well-known and popular FIS model with neural learning capability. This finding is important for the modeling of a monotone FIS model, because it shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course of developing data-driven FIS models. |
| first_indexed | 2025-11-15T06:47:19Z |
| format | Book Chapter |
| id | unimas-15755 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:47:19Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-157552017-04-12T02:55:27Z http://ir.unimas.my/id/eprint/15755/ Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng QA Mathematics The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and normal trapezoid fuzzy sets are used as fuzzy membership functions. Besides that, a strong fuzzy partition strategy is used. Our empirical analysis shows that a set of multi-attribute monotone data may lead to non-monotone fuzzy If-Then rules. The same observation can be made, empirically, using adaptive neuro-fuzzy inference system (ANFIS), a well-known and popular FIS model with neural learning capability. This finding is important for the modeling of a monotone FIS model, because it shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course of developing data-driven FIS models. Springer 2017 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf Teh, Chin Ying and Tay, Kai Meng and Lim, Cheepeng (2017) Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules. In: Emerging Trends in Neuro Engineering and Neural Computation. Series in BioEngineering, 1 . Springer, Singapore, pp. 255-264. ISBN 978-981-10-3955-3 https://link.springer.com/chapter/10.1007/978-981-10-3957-7_15 DOI 10.1007/978-981-10-3957-7_15 |
| spellingShingle | QA Mathematics Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title | Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title_full | Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title_fullStr | Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title_full_unstemmed | Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title_short | Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules |
| title_sort | monotone data samples do not always generate monotone fuzzy if-then rules |
| topic | QA Mathematics |
| url | http://ir.unimas.my/id/eprint/15755/ http://ir.unimas.my/id/eprint/15755/ http://ir.unimas.my/id/eprint/15755/ http://ir.unimas.my/id/eprint/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf |