Abstract This paper proposes Box-Cox transformation-based annealing robust fuzzy neural networks (ARFNNs) that can be used effectively for function approximated problem with skewness noises. In order to overcome the skewness noises problem, the Box-Cox transformation that its object is usually to make residuals more homogeneous in regression, or transform data to be normally distributed has been added to the annealing robust fuzzy neural networks. That is, the proposed approach uses Box-Cox transformation for skewness noises problem and support vector regression (SVR) for the number of rule in the simplified fuzzy inference systems. After the initialization, an annealing robust learning algorithm (ARLA) is then applied to adjust the parameters of the Box-Cox transformation-based annealing robust fuzzy neural networks. Simulation results show that the proposed approach has a fast convergent speed and more generalization capability for the function approximated problem with skewness noises.


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    Titel :

    A Fast Identification Algorithm with Skewness Noises under Box-Cox Transformation-Based Annealing Robust Fuzzy Neural Networks


    Beteiligte:
    Chen, Pi-Yun (Autor:in) / Fu, Yu-Yi (Autor:in) / Jeng, Jin-Tsong (Autor:in) / Su, Kuo-Lan (Autor:in)


    Erscheinungsdatum :

    2011-01-01


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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