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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Bibliographic Details
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
Description
Summary: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.

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