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https://hdl.handle.net/10356/2428| Title: | Fuzzy modelling in reinforcement learning | Authors: | Quah, Kian Hong | Keywords: | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence | Issue Date: | 2006 | Source: | Quah, K. H. (2006). Fuzzy modelling in reinforcement learning. Doctoral thesis, Nanyang Technological University, Singapore. | Abstract: | 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. | URI: | https://hdl.handle.net/10356/2428 | DOI: | 10.32657/10356/2428 | Schools: | School of Computer Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
| Appears in Collections: | SCSE Theses |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| QuahKianHong06.pdf | Main report | 14.66 MB | Adobe PDF |  View/Open |
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