1
|
Wu Y, Wang H, Zhang B, Du KL. Using Radial Basis Function Networks for Function Approximation and Classification. ACTA ACUST UNITED AC 2012. [DOI: 10.5402/2012/324194] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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.
Collapse
Affiliation(s)
- Yue Wu
- Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
| | - Hui Wang
- Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
| | - Biaobiao Zhang
- Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
| | - K.-L. Du
- Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1M8
| |
Collapse
|