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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106361


    Title: Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks
    Authors: Hsu, Chun-Fei;Chang, Chun-Wei
    Keywords: fuzzy neural network;recurrent neural network;intelligent control;sine-cosine perturbed function
    Date: 2016-01-15
    Issue Date: 2016-04-22 13:47:25 (UTC+8)
    Publisher: Elsevier BV
    Abstract: In this paper, a recurrent perturbation fuzzy neural network (RPFNN) is used to online approximate an unknown nonlinear term in the system dynamics. A sine-cosine perturbed membership function is used to handle rule uncertainties when it is hard to exactly determine the grade of the value of fuzzy sets. Unlike type-2 fuzzy sets use an extra type reduction operation to find the output, the proposed RPFNN does not require heavy computational loading. Meanwhile, this paper proposes an intelligent dynamic sliding-mode neural control (IDSNC) system which is composed of a neural controller and an exponential compensator.
    Relation: Neurocomputing 173(pt.3), pp.734-743
    DOI: 10.1016/j.neucom.2015.08.024
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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