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


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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.



Edited by:

Wen-Hsiang Hsieh






P. Y. Chen et al., "The Box-Cox Transformation-Based ARFNNs for Identification of Nonlinear MR Damper System with Outliers and Skewness Noises", Applied Mechanics and Materials, Vols. 284-287, pp. 2120-2123, 2013

Online since:

January 2013




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