Modeling and Applying of RBF Neural Network Based on Fuzzy Clustering and Pseudo-Inverse Method

Author

Li, Xiao-fei ; Dong, Jun-hui ; Zhang, Yong-zhi

Author_Institution

Coll. of Mater. Sci. & Eng., Inner Mongolia Univ. of Technol., Hohhot, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

The key of advancing radial basis function neural network (RBFNN) is how to choose the data center and the number of cluster perfectly. In this paper fuzzy C-means clustering is used as K-means clustering ameliorated algorithm to determine the data center of RBF neural networks hidden nodes. Combined pseudo-inverse method RBF network model is constructed. Simulation test on the dimension predicting in selective laser sintering process showed the model is able to provide higher accurate predict, as well as less calculate quantity and quick training.

Keywords

fuzzy set theory; pattern clustering; radial basis function networks; K-means clustering; RBF network model; RBF neural network; fuzzy C-means clustering; fuzzy clustering; pseudoinverse method; radial basis function neural network; Clustering algorithms; Data engineering; Fuzzy neural networks; Laser modes; Laser sintering; Materials science and technology; Neural networks; Power engineering and energy; Predictive models; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5362683

Filename

5362683