Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index
In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in...
| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/2736/ http://ir.unimas.my/id/eprint/2736/1/Building%20Monotonicity-Preserving%20Fuzzy%20Inference%20Models%20with%20Optimization-Based%20Similarity%20Reasoning%20and%20a%20Monotonicity%20Index.pdf |
| _version_ | 1848835056596942848 |
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| author | Kai, M.T Chee, P.L Tze, L.J |
| author_facet | Kai, M.T Chee, P.L Tze, L.J |
| author_sort | Kai, M.T |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models. |
| first_indexed | 2025-11-15T06:01:48Z |
| format | Proceeding |
| id | unimas-2736 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:01:48Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-27362015-03-24T00:49:09Z http://ir.unimas.my/id/eprint/2736/ Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index Kai, M.T Chee, P.L Tze, L.J TK Electrical engineering. Electronics Nuclear engineering In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models. IEEE 2012 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/2736/1/Building%20Monotonicity-Preserving%20Fuzzy%20Inference%20Models%20with%20Optimization-Based%20Similarity%20Reasoning%20and%20a%20Monotonicity%20Index.pdf Kai, M.T and Chee, P.L and Tze, L.J (2012) Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index. In: WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012. |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Kai, M.T Chee, P.L Tze, L.J Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title | Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title_full | Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title_fullStr | Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title_full_unstemmed | Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title_short | Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index |
| title_sort | building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://ir.unimas.my/id/eprint/2736/ http://ir.unimas.my/id/eprint/2736/1/Building%20Monotonicity-Preserving%20Fuzzy%20Inference%20Models%20with%20Optimization-Based%20Similarity%20Reasoning%20and%20a%20Monotonicity%20Index.pdf |