Model selection for RBF network via generalized degree of freedom

Authors:
Pengcheng Xu;A. W. Jayawardena;W. K. Li
Affiliations:
Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China;International Centre for Water Hazard and Risk Management, Public Works Research Institute, Tsukuba, Japan;Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
Venue:
Neurocomputing
Year:
2013

Citing 7
Cited 1

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Abstract

Radial basis function (RBF) networks are considered in this study to simulate and forecast a chaotic time series. In order to evaluate the performance of the RBF networks, a new method is developed to calculate the generalized degree of freedom (GDF), which is used to obtain an unbiased estimation of variance of the fitted model error for the network. Numerical results show that the proposed estimation of GDF is more stable and faster than that obtained by the Monte Carlo method. A model selection method using GDF for a chaotic time series is then introduced and applied to four chaotic time series. The numerical results show that the network selected by the proposed method gives better prediction ability.