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Using Radial Basis Function Networks for Function Approximation and Classification
Joint Authors
Du, K.-L.
Zhang, Biaobiao
Wu, Yue
Wang, Hui
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-34, 34 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-06
Country of Publication
Egypt
No. of Pages
34
Main Subjects
Abstract EN
The radial basis function (RBF) network has its foundation in the conventional approximation theory.
It has the capability of universal approximation.
The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process.
In this paper, we give a comprehensive survey on the RBF network and its learning.
Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described.
We also compare the features and capability of the two models.
American Psychological Association (APA)
Wu, Yue& Wang, Hui& Zhang, Biaobiao& Du, K.-L.. 2012. Using Radial Basis Function Networks for Function Approximation and Classification. ISRN Applied Mathematics،Vol. 2012, no. 2012, pp.1-34.
https://search.emarefa.net/detail/BIM-463508
Modern Language Association (MLA)
Wu, Yue…[et al.]. Using Radial Basis Function Networks for Function Approximation and Classification. ISRN Applied Mathematics No. 2012 (2012), pp.1-34.
https://search.emarefa.net/detail/BIM-463508
American Medical Association (AMA)
Wu, Yue& Wang, Hui& Zhang, Biaobiao& Du, K.-L.. Using Radial Basis Function Networks for Function Approximation and Classification. ISRN Applied Mathematics. 2012. Vol. 2012, no. 2012, pp.1-34.
https://search.emarefa.net/detail/BIM-463508
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-463508