Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/106361

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题名:	Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks
作者:	Hsu, Chun-Fei;Chang, Chun-Wei
关键词:	fuzzy neural network;recurrent neural network;intelligent control;sine-cosine perturbed function
日期:	2016-01-15
上传时间:	2016-04-22 13:47:25 (UTC+8)
出版者:	Elsevier BV
摘要:	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.
關聯:	Neurocomputing 173(pt.3), pp.734-743
DOI:	10.1016/j.neucom.2015.08.024
显示于类别:	[電機工程學系暨研究所] 期刊論文

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