Fuzzy modelling in reinforcement learning

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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Main Author: Quah, Kian Hong
Other Authors: Quek Hiok Chai
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:https://hdl.handle.net/10356/2428
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Institution: Nanyang Technological University
id sg-ntu-dr.10356-2428
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spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Quah, Kian Hong
Fuzzy modelling in reinforcement learning
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.
author2 Quek Hiok Chai
author_facet Quek Hiok Chai
Quah, Kian Hong
format Theses and Dissertations
author Quah, Kian Hong
author_sort Quah, Kian Hong
title Fuzzy modelling in reinforcement learning
title_short 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_sort fuzzy modelling in reinforcement learning
publishDate 2008
url https://hdl.handle.net/10356/2428
_version_ 1759854760953905152

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