Rough set-based neuro-fuzzy system.
Neuro-fuzzy systems is a popular hybridization in soft computing that abstracts a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic...
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| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/2487 |
| _version_ | 1826113340451061760 |
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| author | Ang, Kai Keng. |
| author2 | Quek, Hiok Chai |
| author_facet | Quek, Hiok Chai Ang, Kai Keng. |
| author_sort | Ang, Kai Keng. |
| collection | NTU |
| description | Neuro-fuzzy systems is a popular hybridization in soft computing that abstracts a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises if-then fuzzy rules and linguistic terms described by membership functions. However, modeling data using neuro-fuzzy systems involves the contradictory requirements of interpretability versus accuracy. Prevailing research that focused on accuracy employed optimization that resulted in membership functions that derailed from human-interpretable linguistic terms. In addition, the modeling of high-dimensional data requires a large number of if-then fuzzy rules that exceeds human level interpretation. This thesis focuses on increasing interpretability without compromising accuracy using a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS), which synergizes rough set-based knowledge reduction with neuro-fuzzy
systems. RNFS directly addresses the problems with the following contributions. |
| first_indexed | 2024-10-01T03:21:36Z |
| format | Thesis |
| id | ntu-10356/2487 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T03:21:36Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/24872023-03-04T00:39:31Z Rough set-based neuro-fuzzy system. Ang, Kai Keng. Quek, Hiok Chai School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Neuro-fuzzy systems is a popular hybridization in soft computing that abstracts a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises if-then fuzzy rules and linguistic terms described by membership functions. However, modeling data using neuro-fuzzy systems involves the contradictory requirements of interpretability versus accuracy. Prevailing research that focused on accuracy employed optimization that resulted in membership functions that derailed from human-interpretable linguistic terms. In addition, the modeling of high-dimensional data requires a large number of if-then fuzzy rules that exceeds human level interpretation. This thesis focuses on increasing interpretability without compromising accuracy using a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS), which synergizes rough set-based knowledge reduction with neuro-fuzzy systems. RNFS directly addresses the problems with the following contributions. DOCTOR OF PHILOSOPHY (SCE) 2008-09-17T09:04:00Z 2008-09-17T09:04:00Z 2008 2008 Thesis Ang, K. K. (2008). Rough set-based neuro-fuzzy system. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2487 10.32657/10356/2487 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ang, Kai Keng. Rough set-based neuro-fuzzy system. |
| title | Rough set-based neuro-fuzzy system. |
| title_full | Rough set-based neuro-fuzzy system. |
| title_fullStr | Rough set-based neuro-fuzzy system. |
| title_full_unstemmed | Rough set-based neuro-fuzzy system. |
| title_short | Rough set-based neuro-fuzzy system. |
| title_sort | rough set based neuro fuzzy system |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/2487 |
| work_keys_str_mv | AT angkaikeng roughsetbasedneurofuzzysystem |