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dc.contributor.authorHsu, Chun-Feien_US
dc.contributor.authorLin, Ping-Zongen_US
dc.contributor.authorLee, Tsu-Tianen_US
dc.contributor.authorWang, Chi-Hsuen_US
dc.date.accessioned2014-12-08T15:10:46Z-
dc.date.available2014-12-08T15:10:46Z-
dc.date.issued2008-10-16en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.fss.2008.01.034en_US
dc.identifier.urihttp://hdl.handle.net/11536/8242-
dc.description.abstractThis paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phases to online mimic an ideal controller, simultaneously. The structure learning phase consists of the growing-and-pruning algorithms of fuzzy rules to achieve an optimal network structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The robust controller is designed to compensate for the modeling error between the SFNN controller and the ideal controller. An online training methodology is developed in the Lyapunov sense, and thus the stability of the closed-loop control system can be guaranteed. Finally, the proposed ASAFNC system is applied to a second-order chaotic dynamics system. The simulation results show that the proposed ASAFNC can achieve favorable tracking performance. (C) 2008 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectfuzzy neural networken_US
dc.subjectasymmetric Gaussian membership functionen_US
dc.subjectstructure adaptation algorithmen_US
dc.subjectadaptive controlen_US
dc.titleAdaptive asymmetric fuzzy neural network controller design via network structuring adaptationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.fss.2008.01.034en_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume159en_US
dc.citation.issue20en_US
dc.citation.spage2627en_US
dc.citation.epage2649en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000259130600001-
dc.citation.woscount11-
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