Research on RBFNN Modeling Based on ICA Feature Extraction

Jie Liu; Ya Rong Wu; Ke Qiao

doi:10.4028/www.scientific.net/AMM.540.460

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 540Research on RBFNN Modeling Based on ICA Feature...

Research on RBFNN Modeling Based on ICA Feature Extraction

Abstract:

In order to accurately depict the complicated characteristics of nonlinear system by modeling, a Radial Basis Function Network (RBFNN) modeling method based on Independent Component Analysis (ICA) is proposed. First ICA is perform for extracting basic features of the training samples, and then the extracted basic features is used to establish to RBFNN model. The simulation indicates that, the hybrid modeling method proposed is better than that of another 2 methods with simple model structure, and is effective and feasible to establish for the nonlinear modeling system.

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Periodical:

Applied Mechanics and Materials (Volume 540)

Pages:

460-463

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.540.460

Citation:

Cite this paper

Online since:

April 2014

Authors:

Jie Liu*, Ya Rong Wu, Ke Qiao

Keywords:

Feature Extraction, Independent Component Analysis (ICA), Nonlinear System, Radial Basis Function Neural Network

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* - Corresponding Author

References

[1] S. Chert, P. M. Crant, C. F. N. Cown. Orthogonal least square algorithm for radial basis function networks. IEEE Transaction on Neural Networks, 2(2), (1991), 302-309.

DOI: 10.1109/72.80341

Google Scholar

[2] S. Chert, S. A. Billings, P. M. Grant. Recursive hybrid algorithm for nonlinear system identification using radial basis function networks. International Journal of Control, 55(5), (1992), 1051-1070.

DOI: 10.1080/00207179208934272

Google Scholar

[3] Y. Gu, J. H. Zheng, J. Sun, et al. Intrusion detection method based on independent component analysis and support vector machine. Journal of Xi'an Jiaotong University, 39(8), (2005), 876-879.

Google Scholar

[4] J. F. Cardoso. High-order contrasts for independent component analysis. Neural Computation, 11(1), (1999), 157-192.

DOI: 10.1162/089976699300016863

Google Scholar

[5] A. Hyvärinen, E. Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), (2000), 411-430.

DOI: 10.1016/s0893-6080(00)00026-5

Google Scholar