کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407891 | 678237 | 2013 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Model selection for RBF network via generalized degree of freedom
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 99, 1 January 2013, Pages 163–171
Journal: Neurocomputing - Volume 99, 1 January 2013, Pages 163–171
نویسندگان
Pengcheng Xu, A.W. Jayawardena, W.K. Li,