Construction of compact RBF network by refining coarse clusters and widths 被引量：1

Construction of compact RBF network by refining coarse clusters and widths

下载PDF

导出

摘要 It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function（RBF） network.This paper aims at constructing a compact RBF network with two main steps.In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree（MDST）.Meanwhile the coarse widths are obtained.In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths.Experiments of the proposed algorithm show its great performance in times series prediction and classification. It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function（RBF） network.This paper aims at constructing a compact RBF network with two main steps.In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree（MDST）.Meanwhile the coarse widths are obtained.In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths.Experiments of the proposed algorithm show its great performance in times series prediction and classification.

作者 Zeng Delu Zhou Zhiheng Xie Shengli

机构地区 School of Electronic and Information Engineering

出处《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第6期1309-1315,共7页 系统工程与电子技术（英文版）

基金 supported by Key Program of National Natural Science Foundation of China (U0635001) China Postdoctoral Science Foundation (20060390728) the Natural Science Fund of Guangdong Province, China (07006490)

关键词 CLUSTERING anisotropic gradient descent radial basis function time series prediction boundary extraction. clustering, anisotropic gradient descent, radial basis function, time series prediction, boundary extraction.

分类号 TP18 [自动化与计算机技术—控制理论与控制工程] O174.41 [理学—基础数学]

引文网络
相关文献

参考文献15

1Broomhead D S, Lowe D. Multivariable functional interpolation and adaptive networks. Complex System, 1988, 2: 321-355.
2Chen S, Cowan C F N, Grant P M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. on Neural Networks, 1991, 2(2): 302- 309.
3Orr M J L. Regularization on the selection of radial basis function centers. Neural Computation, 1995, 7(3): 606- 623.
4Huang G B, Saratchandran P, Sundararajan N. A generalized growing and pruning RBF(GGAP-RBF) neural network for function approximation. IEEE Trans. on Neural network, 2005, 16(1): 57-67.
5Chen S, Chng E S, Alkadhimi K. Regularized orthogonal least squares algorithm for constructing radial basis function networks. International Journal of Control, 1996, 64(5): 829-837.
6Panchapakesan C, Palaniswami M, Ralph D, et al. Effects of moving the centers in an RBF network. IEEE Trans. on Neural Network, 2002, 13(6): 1299-1307.
7Huang G B, Saratchandran P, Sundarrajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans. on Systems, Man, and Cybernetics, Part B-Cybernetics, 2004, 34(6): 2284-2292.
8Yeung D S, Ng W W Y, Wang D, et al. Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans. on Neural Networks, 2007, 18(5): 1294 -1305.
9Chen S, Labib K, Hanzo L. Clustering-based symmetric radial basis function beamforming. IEEE Signal Processing Letters, 2007, 14(9): 589-592.
10Lin B S, Chong F C, Lai F. higher-order-statistics-based radial basis function networks for signal enhancement. IEEE Trans. on Neural Networks, 2007, 18(3): 823- 832.