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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (2): 131-138    DOI: 10.1631/jzus.C1100176
    
Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
Xin-zheng Xu, Shi-fei Ding, Zhong-zhi Shi, Hong Zhu
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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摘要： 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.
关键词： Radial basis function neural network (RBFNN);  Rough sets;  Affinity propagation;  Clustering    
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.
Key words: Radial basis function neural network (RBFNN)    Rough sets    Affinity propagation    Clustering
收稿日期: 2011-06-25 出版日期: 2012-01-19
CLC:  TP183  
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Xin-zheng Xu
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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. Front. Inform. Technol. Electron. Eng., 2012, 13(2): 131-138.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100176    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I2/131

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