Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/4204
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dc.contributor.authorDeng, Changen
dc.date.accessioned2008-09-17T09:46:38Zen
dc.date.available2008-09-17T09:46:38Zen
dc.date.copyright2005en
dc.date.issued2005en
dc.identifier.citationDeng, C. (2005). Fuzzy reinforcement learning and its applications to mobile robot navigation. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/4204en
dc.description.abstractFuzzy logic is a mathematical approach to emulate the human way of thinking. It has been shown that fuzzy logic could serve as a powerful methodology for dealing with imprecision and nonlinearity efficiently. However, the conventional way of designing a fuzzy system has been a subjective approach. If the fuzzy system somehow possesses learning abilities, an enormous amount of human efforts would be saved from tuning the system.en
dc.rightsNanyang Technological Universityen
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Roboticsen
dc.titleFuzzy reinforcement learning and its applications to mobile robot navigationen
dc.typeThesisen
dc.contributor.supervisorEr Meng Jooen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en
dc.identifier.doi10.32657/10356/4204en
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