Fuzzy modelling in reinforcement learning
A generic Fuzzy Input Takagi-Sugeno-Kang fuzzy framework (FITSK) is proposed to handle the different scenarios in this design problem. The online learning FITSK framework is extensible to both the zero-order and the first-order FITSK models. A localized version of Kalman filter algorithm is propose...
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| 格式: | Thesis |
| 出版: |
2008
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| 在线阅读: | https://hdl.handle.net/10356/2428 |
| _version_ | 1826117418314891264 |
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| author | Quah, Kian Hong |
| author2 | Quek Hiok Chai |
| author_facet | Quek Hiok Chai Quah, Kian Hong |
| author_sort | Quah, Kian Hong |
| collection | NTU |
| description | A generic Fuzzy Input Takagi-Sugeno-Kang fuzzy framework (FITSK) is proposed to handle the different scenarios in this design problem. The online learning FITSK framework is extensible to both the zero-order and the first-order FITSK models. A localized version of Kalman filter algorithm is proposed for the parameter tuning of the first-order FITSK model. |
| first_indexed | 2024-10-01T04:26:56Z |
| format | Thesis |
| id | ntu-10356/2428 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T04:26:56Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/24282023-03-04T00:36:00Z Fuzzy modelling in reinforcement learning Quah, Kian Hong Quek Hiok Chai School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence A generic Fuzzy Input Takagi-Sugeno-Kang fuzzy framework (FITSK) is proposed to handle the different scenarios in this design problem. The online learning FITSK framework is extensible to both the zero-order and the first-order FITSK models. A localized version of Kalman filter algorithm is proposed for the parameter tuning of the first-order FITSK model. DOCTOR OF PHILOSOPHY (SCE) 2008-09-17T09:02:45Z 2008-09-17T09:02:45Z 2006 2006 Thesis Quah, K. H. (2006). Fuzzy modelling in reinforcement learning. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2428 10.32657/10356/2428 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Quah, Kian Hong Fuzzy modelling in reinforcement learning |
| title | Fuzzy modelling in reinforcement learning |
| title_full | Fuzzy modelling in reinforcement learning |
| title_fullStr | Fuzzy modelling in reinforcement learning |
| title_full_unstemmed | Fuzzy modelling in reinforcement learning |
| title_short | Fuzzy modelling in reinforcement learning |
| title_sort | fuzzy modelling in reinforcement learning |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/2428 |
| work_keys_str_mv | AT quahkianhong fuzzymodellinginreinforcementlearning |