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CLC number: TP183

On-line Access: 2012-01-19

Received: 2011-06-25

Revision Accepted: 2011-10-25

Crosschecked: 2011-12-29

Cited: 12

Clicked: 4688

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.2 P.131-138

http://doi.org/10.1631/jzus.C1100176


Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm


Author(s):  Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu

Affiliation(s):  School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; more

Corresponding email(s):   xuxinzh@163.com

Key Words:  Radial basis function neural network (RBFNN), Rough sets, Affinity propagation, Clustering


Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm[J]. Journal of Zhejiang University Science C, 2012, 13(2): 131-138.

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author="Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu",
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%T Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
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%A Shi-fei Ding
%A Zhong-zhi Shi
%A Hong Zhu
%J Journal of Zhejiang University SCIENCE C
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%N 2
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100176

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T1 - Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
A1 - Xin-zheng Xu
A1 - Shi-fei Ding
A1 - Zhong-zhi Shi
A1 - Hong Zhu
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 2
SP - 131
EP - 138
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100176


Abstract: 
A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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