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

doi:10.1631/jzus.C1100176

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

计算机与自动化 | Updated：2022-12-09

- Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
- Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm
- 信息与电子工程前沿（英文） 2012年13卷第2期页码：131-138
- Affiliations：
  
  1.School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
  2.Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- Author bio：
  
  [ "Xin-zheng XU, E-mail: xuxinzh@163.com" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 41074003 and 60975039), the Opening Foundation of the Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No. IIP2010-1), and the Youth Science Foundation of China University of Mining and Technology (Nos. 2008A045 and 2009A053)
- DOI：10.1631/jzus.C1100176
  中图分类号： TP183
- 收稿：2011-06-25，
  
  修回：2011-12-29，
  
  纸质出版：2012-02
- Accepted：
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Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm[J]. 信息与电子工程前沿（英文）, 2012,13(2):131-138.

Xin-zheng XU, Shi-fei DING, Zhong-zhi SHI, et al. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(2): 131-138.
Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm[J]. 信息与电子工程前沿（英文）, 2012,13(2):131-138. DOI： 10.1631/jzus.C1100176.

Xin-zheng XU, Shi-fei DING, Zhong-zhi SHI, et al. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(2): 131-138. DOI： 10.1631/jzus.C1100176.

摘要

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.

关键词

Keywords

references

S. Beyhan , , , M. Alci . . Stable modeling based control methods using a new RBF network . . ISA Trans. , , 2010 . . 49 ( ( 4 ): ): 510 - - 518 . . DOI: 10.1016/j.isatra.2010.04.005 http://doi.org/10.1016/j.isatra.2010.04.005 . .

C.L. Blake , , , C.J. Merz . . UCI Repository of Machine Learning Databases. 1998. Department of Information and Computer Science, University of California, Irvine, CA . . Available from , , 1998 . . [Accessed on Sept. 23, 2011] http://www.ics.uci.edu/mlearn/Machine-Learning.html http://www.ics.uci.edu/mlearn/Machine-Learning.html , , . .

S. Chen , , , C.F.N. Cowan , , , P.M. Grant . . Orthogonal least squares learning algorithm for radial basis function networks . . IEEE Trans. Neur. Network , , 1991 . . 2 ( ( 2 ): ): 302 - - 309 . . DOI: 10.1109/72.80341 http://doi.org/10.1109/72.80341 . .

J.H. Cheng , , , H.P. Chen , , , Y.M. Lin . . A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5 . . Exp. Syst. Appl. , , 2010 . . 37 ( ( 3 ): ): 1814 - - 1820 . . DOI: 10.1016/j.eswa.2009.07.019 http://doi.org/10.1016/j.eswa.2009.07.019 . .

L.S. Coelho , , , A.A.P. Santos . . A RBF neural network model with GARCH errors: application to electricity price forecasting . . Electr. Power Syst. Res. , , 2011 . . 81 ( ( 1 ): ): 74 - - 83 . . DOI: 10.1016/j.epsr.2010.07.015 http://doi.org/10.1016/j.epsr.2010.07.015 . .

D.J. Du , , , M.R. Fei , , , L.X. Li . . Radial-basis-function neural network based on fast recursive algorithm and its application . . Control Theory Appl. , , 2008 . . 25 ( ( 5 ): ): 827 - - 830 . . . .

D.J. Du , , , K. Li , , , M.R. Fei . . A fast multi-output RBF neural network construction method . . Neurocomputing , , 2010 . . 73 ( ( 10-12 ): ): 2196 - - 2202 . . DOI: 10.1016/j.neucom.2010.01.014 http://doi.org/10.1016/j.neucom.2010.01.014 . .

B.J. Frey , , , D. Dueck . . Clustering by passing messages between data points . . Science , , 2007 . . 315 ( ( 5814 ): ): 972 - - 976 . . DOI: 10.1126/science.1136800 http://doi.org/10.1126/science.1136800 . .

A.F. Guerra , , , L. Coelho . . Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization . . Chaos Sol. Fract. , , 2008 . . 35 ( ( 5 ): ): 967 - - 979 . . DOI: 10.1016/j.chaos.2006.05.077 http://doi.org/10.1016/j.chaos.2006.05.077 . .

M.Z. Hou , , , X.L. Han . . The multidimensional function approximation based on constructive wavelet RBF neural network . . Appl. Soft Comput. , , 2011 . . 11 ( ( 2 ): ): 2173 - - 2177 . . DOI: 10.1016/j.asoc.2010.07.016 http://doi.org/10.1016/j.asoc.2010.07.016 . .

T. Jayasree , , , D. Devaraj , , , R. Sukanesh . . Power quality disturbance classification using Hilbert transform and RBF networks . . Neurocomputing , , 2010 . . 73 ( ( 7-9 ): ): 1451 - - 1456 . . DOI: 10.1016/j.neucom.2009.11.008 http://doi.org/10.1016/j.neucom.2009.11.008 . .

X.Y. Jing , , , Y.F. Yao , , , J.Y. Yang , , , D. Zhang . . A novel face recognition approach based on kernel discriminative common vectors (KDCV) feature extraction and RBF neural network . . Neurocomputing , , 2008 . . 71 ( ( 13-15 ): ): 3044 - - 3048 . . DOI: 10.1016/j.neucom.2007.08.027 http://doi.org/10.1016/j.neucom.2007.08.027 . .

C.M. Lee , , , C.N. Ko . . Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm . . Neurocomputing , , 2009 . . 73 ( ( 1-3 ): ): 449 - - 460 . . DOI: 10.1016/j.neucom.2009.07.005 http://doi.org/10.1016/j.neucom.2009.07.005 . .

K.Z. Mao . . RBF neural network center selection based on Fisher ratio class separability measure . . IEEE Trans. Neur. Networks , , 2002 . . 13 ( ( 5 ): ): 1211 - - 1217 . . DOI: 10.1109/TNN.2002.1031953 http://doi.org/10.1109/TNN.2002.1031953 . .

K.Z. Mao , , , G.B. Huang . . Neuron selection for RBF neural network classifier based on data structure preserving criterion . . IEEE Trans. Neur. Networks , , 2005 . . 16 ( ( 6 ): ): 1531 - - 1540 . . DOI: 10.1109/TNN.2005.853575 http://doi.org/10.1109/TNN.2005.853575 . .

H.S. Park , , , Y.D. Chung , , , S.K. Oh , , , W. Pedrycz , , , H.K. Kim . . Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet . . Eng. Appl. Artif. Intell. , , 2011 . . 24 ( ( 3 ): ): 543 - - 554 . . DOI: 10.1016/j.engappai.2010.11.001 http://doi.org/10.1016/j.engappai.2010.11.001 . .

Z. Pawlak , , , J. Grzymala-Busse , , , R. Slowinski , , , W. Ziarko . . Rough sets . . Commun. ACM , , 1995 . . 38 ( ( 11 ): ): 88 - - 95 . . DOI: 10.1145/219717.219791 http://doi.org/10.1145/219717.219791 . .

N. Qu , , , H. Mi , , , B. Wang , , , Y.L. Ren . . Application of GA-RBF networks to the nondestructive determination of active component in pharmaceutical powder by NIR spectroscopy . . J. Taiwan Inst. Chem. Eng. , , 2009 . . 40 ( ( 2 ): ): 162 - - 167 . . DOI: 10.1016/j.jtice.2008.08.002 http://doi.org/10.1016/j.jtice.2008.08.002 . .

W. Shen , , , X.P. Guo , , , C. Wu , , , D.S. Wu . . Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm . . Knowl.- Based Syst. , , 2011 . . 24 ( ( 3 ): ): 378 - - 385 . . DOI: 10.1016/j.knosys.2010.11.001 http://doi.org/10.1016/j.knosys.2010.11.001 . .

S. Su , , , X.Z. Duan , , , W.L. Chan , , , Z.H. Li . . Erroneous measurement detection in substation automation system using OLS based RBF neural network . . Int. J. Electr. Power Energy Syst. , , 2009 . . 31 ( ( 7-8 ): ): 351 - - 355 . . DOI: 10.1016/j.ijepes.2009.03.008 http://doi.org/10.1016/j.ijepes.2009.03.008 . .

X.J. Wu , , , X.J. Zhu , , , G.Y. Cao , , , H.Y. Tu . . Predictive control of SOFC based on a GA-RBF neural network model . . J. Power Sources , , 2008 . . 179 ( ( 1 ): ): 232 - - 239 . . DOI: 10.1016/j.jpowsour.2007.12.036 http://doi.org/10.1016/j.jpowsour.2007.12.036 . .

D.Y. Xia , , , F. Wu , , , X.Q. Zhang , , , Y.T. Zhuang . . Local and global approaches of affinity propagation clustering for large scale data . . J. Zhejiang Univ.-Sci. A , , 2008 . . 9 ( ( 10 ): ): 1373 - - 1381 . . DOI: 10.1631/jzus.A0720058 http://doi.org/10.1631/jzus.A0720058 . .

D.L. Yu , , , J.B. Gomm , , , D. Williams . . A recursive orthogonal least squares algorithm for training RBF networks . . Neur. Process. Lett. , , 1997 . . 5 ( ( 3 ): ): 167 - - 176 . . DOI: 10.1023/A:1009622226531 http://doi.org/10.1023/A:1009622226531 . .

A.S. Zhang , , , L. Zhang . . RBF neural networks for the prediction of building interference effects . . Comput. Struct. , , 2004 . . 82 ( ( 27 ): ): 2333 - - 2339 . . DOI: 10.1016/j.compstruc.2004.05.014 http://doi.org/10.1016/j.compstruc.2004.05.014 . .

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