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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

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Citations:  Bibtex RefMan EndNote GB/T7714

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


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

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

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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