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

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 attrib...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering Vol. 13; no. 2; pp. 131 - 138
Main Authors: Xu, Xin-zheng, Ding, Shi-fei, Shi, Zhong-zhi, Zhu, Hong
Format: Journal Article
Language:English
Published: Heidelberg SP Zhejiang University Press 01.02.2012
Springer Nature B.V
Subjects:
Affinity
Algorithms
Clustering
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Networks
Neural networks
Optimization
Radial basis function
RBFNN
RBF神经网络
优化
属性约简
径向基函数神经网络
性传播
粗糙集理论
聚类算法
Radial basis function neural network (RBFNN)
TP183
Clustering
Rough sets
Affinity propagation
ISSN:1869-1951, 2095-9184, 1869-196X, 2095-9230
Online Access:Get full text
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Summary: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.
Bibliography:Radial basis function neural network （RBFNN）, Rough sets, Affinity propagation, Clustering
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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ISSN:1869-1951
2095-9184
1869-196X
2095-9230
DOI:10.1631/jzus.C1100176