Using Radial Basis Function Networks for Function Approximation and Classification. (6th March 2012)

• Record Type: Journal Article
• Authors: Wu, Yue; Wang, Hui; Zhang, Biaobiao; Du, K.-L.
• Other Names: Gallopoulos E. Academic Editor.; Kita E. Academic Editor.; Sun M. Academic Editor.
• Is Part Of: ISRN applied mathematics. Volume 2012(2012)
• Publication Date: 2012-03-06
• Subjects: Mathematics -- Periodicals; Mathematics; Periodicals; Electronic journals; 510
• DOI: 10.5402/2012/324194
• Languages: English
• ISSNs: 2090-5564

Abstract

Abstract : 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.