On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models
Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A m...
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| Format: | Article |
| Language: | English |
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nstitute of Electrical and Electronics Engineers Inc.
2009
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| Online Access: | http://ir.unimas.my/id/eprint/18482/ http://ir.unimas.my/id/eprint/18482/1/05277387%20%28abstrak%29.pdf |
| _version_ | 1848838522556907520 |
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| author | Tay, Kai Mei Chee, Peng Lim |
| author_facet | Tay, Kai Mei Chee, Peng Lim |
| author_sort | Tay, Kai Mei |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc |
| first_indexed | 2025-11-15T06:56:54Z |
| format | Article |
| id | unimas-18482 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:56:54Z |
| publishDate | 2009 |
| publisher | nstitute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-184822017-11-13T06:36:24Z http://ir.unimas.my/id/eprint/18482/ On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models Tay, Kai Mei Chee, Peng Lim TJ Mechanical engineering and machinery Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc nstitute of Electrical and Electronics Engineers Inc. 2009-08 Article PeerReviewed text en http://ir.unimas.my/id/eprint/18482/1/05277387%20%28abstrak%29.pdf Tay, Kai Mei and Chee, Peng Lim (2009) On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models. IEEE International Conference on Fuzzy Systems. pp. 1739-1740. ISSN 1098-7584 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5277387 |
| spellingShingle | TJ Mechanical engineering and machinery Tay, Kai Mei Chee, Peng Lim On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title | On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title_full | On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title_fullStr | On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title_full_unstemmed | On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title_short | On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| title_sort | on the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models |
| topic | TJ Mechanical engineering and machinery |
| url | http://ir.unimas.my/id/eprint/18482/ http://ir.unimas.my/id/eprint/18482/ http://ir.unimas.my/id/eprint/18482/1/05277387%20%28abstrak%29.pdf |