Integration of ANN and Statistical Method for Outlier Detection in Complex System
نویسندگان
چکیده
In this paper, an outlier detection method based on radial basis functions–principal component analysis (RBF-PCA) approach and Prescott method, a statistical detection approach, is proposed to detect the outlier in the complex system without clear mechanisms. Making full use of the capacity of neural networks on nonlinear mapping and the effect of Prescott method on outlier detection in linear model, the integration of two approaches makes the outlier detection in complex nonlinear system more convenient, reliable, and precise. The experiments show us the satisfactory effects of the proposed method and its superiority over some distances based methods. Furthermore, some rules were discussed for the wide use of the proposed integrated method
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