The Box-Cox Transformation-Based ARFNNs for Identification of Nonlinear MR Damper System with Outliers and Skewness Noises

勤益科大機構典藏 > 電資學院 > 電機工程系(所) > 【電機工程系所】期刊論文 > Item 987654321/7072

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題名:	The Box-Cox Transformation-Based ARFNNs for Identification of Nonlinear MR Damper System with Outliers and Skewness Noises
作者:	陳碧雲
貢獻者:	電機工程(學)系
日期:	2012-01
上傳時間:	2018-01-07 11:27:48 (UTC+8)
摘要:	In this paper, the Box–Cox transformation-based annealing robust fuzzy neural networks (ARFNNs) are proposed for identification of the nonlinear Magneto-rheological (MR) damper with outliers and skewness noises. Firstly, utilizing the Box-Cox transformation that its object is usually to make residuals more homogeneous in regression, or transform data to be normally distributed. Consequently, a support vector regression (SVR) method with Gaussian kernel function has the good performance to determine the number of rule in the simplified fuzzy inference systems and initial weights in the fuzzy neural networks. Finally, the annealing robust learning algorithm (ARLA) can be used effectively to adjust the parameters of the Box-Cox transformation-based ARFNNs. Simulation results show the superiority of the proposed method for the nonlinear MR damper systems with outliers and skewness noises
關聯:	Applied Mechanics and Materials
顯示於類別:	[電機工程系(所)] 【電機工程系所】期刊論文

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