期刊
NEUROCOMPUTING
卷 168, 期 -, 页码 1132-1137出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.05.010
关键词
Extreme learning machine; Radial basis function networks; Universal consistency
资金
- National 973 Program of China [2013CB329404]
- NSF Key Project of China [11131006, 91330204]
This paper concerns the universal consistency of extreme learning machine (ELM) for radial basis function networks (RBFNs). That is, the estimator constructed by ELM for RBFNs learning system can approximate an arbitrary regression function to any accuracy, as long as the number of the training samples is sufficiently large. Furthermore, we also give the conditions for the kernel functions, with which the corresponding ELM-RBFNs estimator is strongly universal consistency. These results not only underlie the feasibility of ELM for RBFNs case, but also provide guidance of practical selection for kernel functions in ELM application. (C) 2015 Elsevier B.V. All rights reserved.
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