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

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.

@article{title="Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm",

author="Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu",

journal="Journal of Zhejiang University Science C",

volume="13",

number="2",

pages="131-138",

year="2012",

publisher="Zhejiang University Press & Springer",

doi="10.1631/jzus.C1100176"

}

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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

%V 13

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%P 131-138

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%D 2012

%I Zhejiang University Press & Springer

%DOI 10.1631/jzus.C1100176

TY - JOUR

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

ER -

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.

**
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