Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44372
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorHsu, C.H.en_US
dc.date2009zh_TW
dc.date.accessioned2014-06-06T08:12:12Z-
dc.date.available2014-06-06T08:12:12Z-
dc.identifier.issn1083-4419zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44372-
dc.description.abstractThis paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q-values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS.en_US
dc.language.isoen_USzh_TW
dc.relationIeee Transactions on Systems Man and Cybernetics Part B-Cyberneticsen_US
dc.relation.ispartofseriesIeee Transactions on Systems Man and Cybernetics Part B-Cybernetics, Volume 39, Issue 6, Page(s) 1528-1542.en_US
dc.relation.urihttp://dx.doi.org/10.1109/tsmcb.2009.2020569en_US
dc.subjectAnt colony optimization (ACO)en_US
dc.subjectfuzzy Q-learningen_US
dc.subjectinterval type-2 fuzzyen_US
dc.subjectsetsen_US
dc.subjectreinforcement learningen_US
dc.subjecttype-2 fuzzy systemsen_US
dc.subjectneural-networken_US
dc.subjectlogic systemsen_US
dc.subjectfpga implementationen_US
dc.subjectsymbiotic evolutionen_US
dc.subjectinference networken_US
dc.subjectinterpretabilityen_US
dc.subjectalgorithmen_US
dc.subjectrobotsen_US
dc.titleReinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimizationen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1109/tsmcb.2009.2020569zh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
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