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Journal of Zhejiang University SCIENCE C
ISSN 1869-1951(Print), 1869-196x(Online), Monthly
2012 Vol.13 No.2 P.131-138
Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
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
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DOI:
10.1631/jzus.C1100176
CLC number:
TP183
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2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2011-12-29