A global learning method of RBFN

Author

Yingjian, Qi ; Siwei, Luo ; Jianyu, Li

Author_Institution

Dept. of Comput. Sci. & Technol., Northern Jiaotong Univ., Beijing, China

Volume

2

fYear

2002

fDate

26-30 Aug. 2002

Firstpage

1195

Abstract

Radial basis function networks have been used successfully in various fields. Since the methods of learning RBFN are often separated into two stages which trend lead to suboptimal results. We proposed the method of using the EM algorithm to training the whole parameters at the same stage such that the parameters are learned globally. The initial parameters are decided by an improved cluster method to alleviate the local minimal problem. We analyze the relationship between the RBFN and the Gaussian mixture model that assure the feasibility of using the EM algorithm in RBFN.

Keywords

Gaussian processes; learning (artificial intelligence); optimisation; parameter estimation; radial basis function networks; EM algorithm; Gaussian mixture model; RBFN; cluster method; global learning method; local minimal problem; parameter estimation; parameters training; radial basis function networks; suboptimal results; Algorithm design and analysis; Artificial neural networks; Broadcasting; Clustering algorithms; Computer science; Electronic mail; Kernel; Learning systems; Multi-layer neural network; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2002 6th International Conference on

Print_ISBN

0-7803-7488-6

Type

conf

DOI

10.1109/ICOSP.2002.1180004

Filename

1180004