Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning
Recent research on Single Input Rule Modules (SIRMs)-connected fuzzy inference system (FIS) focuses on its monotonicity property fulfillment. The aim of this paper is to propose an alternative approach for modeling of monotonicity-preserving SIRMs-connected FIS. A new monotonicity index (MI) for app...
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| Format: | Article |
| Language: | English |
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IEEE
2013
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| Online Access: | http://ir.unimas.my/id/eprint/8363/ http://ir.unimas.my/id/eprint/8363/1/Monotonicity%20Preserving%20SIRMs-Connected%20Fuzzy%20%28abstract%29.pdf |
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| author | See, Hung Lau Tay, Kai Meng Chee, Khoon Ng |
| author_facet | See, Hung Lau Tay, Kai Meng Chee, Khoon Ng |
| author_sort | See, Hung Lau |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Recent research on Single Input Rule Modules (SIRMs)-connected fuzzy inference system (FIS) focuses on its monotonicity property fulfillment. The aim of this paper is to propose an alternative approach for modeling of monotonicity-preserving SIRMs-connected FIS. A new monotonicity index (MI) for approximating the monotonicity property fulfillment of an SIRMs-connected FIS is proposed. A hybrid of Harmony Search (HS), SIRMs-connected FIS, and the new MI is investigated. A proposed data-driven monotonicity-preserving SIRMs-connected FIS model with HS is then presented. The use of MI for tuning of an SIRMs-connected FIS is demonstrated too. |
| first_indexed | 2025-11-15T06:22:39Z |
| format | Article |
| id | unimas-8363 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:22:39Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-83632015-07-28T02:25:43Z http://ir.unimas.my/id/eprint/8363/ Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning See, Hung Lau Tay, Kai Meng Chee, Khoon Ng TA Engineering (General). Civil engineering (General) Recent research on Single Input Rule Modules (SIRMs)-connected fuzzy inference system (FIS) focuses on its monotonicity property fulfillment. The aim of this paper is to propose an alternative approach for modeling of monotonicity-preserving SIRMs-connected FIS. A new monotonicity index (MI) for approximating the monotonicity property fulfillment of an SIRMs-connected FIS is proposed. A hybrid of Harmony Search (HS), SIRMs-connected FIS, and the new MI is investigated. A proposed data-driven monotonicity-preserving SIRMs-connected FIS model with HS is then presented. The use of MI for tuning of an SIRMs-connected FIS is demonstrated too. IEEE 2013 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/8363/1/Monotonicity%20Preserving%20SIRMs-Connected%20Fuzzy%20%28abstract%29.pdf See, Hung Lau and Tay, Kai Meng and Chee, Khoon Ng (2013) Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning. Fuzzy Systems (FUZZ), 2013 IEEE International Conference. pp. 1-7. ISSN 1098-7584 http://www.researchgate.net/publication/261396697_Monotonicity_preserving_SIRMs-connected_fuzzy_inference_systems_with_a_new_monotonicity_index_Learning_and_tuning 10.1109/FUZZ-IEEE.2013.6622355 |
| spellingShingle | TA Engineering (General). Civil engineering (General) See, Hung Lau Tay, Kai Meng Chee, Khoon Ng Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title | Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title_full | Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title_fullStr | Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title_full_unstemmed | Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title_short | Monotonicity preserving SIRMs-connected fuzzy inference systems with a new monotonicity index: Learning and tuning |
| title_sort | monotonicity preserving sirms-connected fuzzy inference systems with a new monotonicity index: learning and tuning |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://ir.unimas.my/id/eprint/8363/ http://ir.unimas.my/id/eprint/8363/ http://ir.unimas.my/id/eprint/8363/ http://ir.unimas.my/id/eprint/8363/1/Monotonicity%20Preserving%20SIRMs-Connected%20Fuzzy%20%28abstract%29.pdf |