Fuzzy integral for rule aggregation in fuzzy inference systems
The fuzzy inference system (FIS) has been tuned and re-vamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/44678/ |
| _version_ | 1848796973295992832 |
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| author | Tomlin, Leary Anderson, Derek T. Wagner, Christian Havens, Timothy C. Keller, James M. |
| author_facet | Tomlin, Leary Anderson, Derek T. Wagner, Christian Havens, Timothy C. Keller, James M. |
| author_sort | Tomlin, Leary |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The fuzzy inference system (FIS) has been tuned and re-vamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires non- additivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to \unrestricted" fuzzy sets, i.e., subnormal and non- convex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions. |
| first_indexed | 2025-11-14T19:56:29Z |
| format | Article |
| id | nottingham-44678 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:56:29Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-446782020-05-08T09:45:18Z https://eprints.nottingham.ac.uk/44678/ Fuzzy integral for rule aggregation in fuzzy inference systems Tomlin, Leary Anderson, Derek T. Wagner, Christian Havens, Timothy C. Keller, James M. The fuzzy inference system (FIS) has been tuned and re-vamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires non- additivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to \unrestricted" fuzzy sets, i.e., subnormal and non- convex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions. Springer 2016-06-20 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/44678/1/IPMU_2016%20Wagner.pdf Tomlin, Leary, Anderson, Derek T., Wagner, Christian, Havens, Timothy C. and Keller, James M. (2016) Fuzzy integral for rule aggregation in fuzzy inference systems. Communications in Computer and Information Science, 610 . pp. 78-90. ISSN 1865-0929 Fuzzy inference system Choquet integral Fuzzy integral gFI NDFI Fuzzy measure https://link.springer.com/chapter/10.1007%2F978-3-319-40596-4_8 doi:10.1007/978-3-319-40596-4_8 doi:10.1007/978-3-319-40596-4_8 |
| spellingShingle | Fuzzy inference system Choquet integral Fuzzy integral gFI NDFI Fuzzy measure Tomlin, Leary Anderson, Derek T. Wagner, Christian Havens, Timothy C. Keller, James M. Fuzzy integral for rule aggregation in fuzzy inference systems |
| title | Fuzzy integral for rule aggregation in fuzzy inference systems |
| title_full | Fuzzy integral for rule aggregation in fuzzy inference systems |
| title_fullStr | Fuzzy integral for rule aggregation in fuzzy inference systems |
| title_full_unstemmed | Fuzzy integral for rule aggregation in fuzzy inference systems |
| title_short | Fuzzy integral for rule aggregation in fuzzy inference systems |
| title_sort | fuzzy integral for rule aggregation in fuzzy inference systems |
| topic | Fuzzy inference system Choquet integral Fuzzy integral gFI NDFI Fuzzy measure |
| url | https://eprints.nottingham.ac.uk/44678/ https://eprints.nottingham.ac.uk/44678/ https://eprints.nottingham.ac.uk/44678/ |