A Nonlinear Blind Source Separation Method Based On Radial Basis Function and Quantum Genetic Algorithm

Pidong Wang; Ruoyun Tang; Di Zhao; Hongyi Li; Jiaxin Chen

doi:10.1051/matecconf/20166101014

All issues

Volume 61 (2016)

MATEC Web of Conferences, 61 (2016) 01014

References

Open Access

Issue		MATEC Web of Conferences Volume 61, 2016 The International Seminar on Applied Physics, Optoelectronics and Photonics (APOP 2016)


Article Number		01014
Number of page(s)		5
Section		Chapter 1 Applied Physics
DOI		https://doi.org/10.1051/matecconf/20166101014
Published online		28 June 2016

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J. A. Yang, Y. Zou, Z. Q. Zhuang. Nonlinear blind source separation algorithm using multi-universe parallel quantum genetic algorithm. Journal of Electronics and Information Technology, 2004, 26(8): 1210–1217. [Google Scholar]
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H. Li, C. Wang, D. Zhao. An Improved EMD and Its Applications to Find the Basis Functions of EMI Signals. Mathematical Problems in Engineering, (2015) [Google Scholar]
H. Li, Z. Song, D. Zhao, P. Wang, J. Chen. A Single Channel EMI Signal Separation Method Based on Directly-mean Empirical Mode Decomposition. Journal of Infromation … Computation Science, 12(17), 6333–6340 (2015) [CrossRef] [Google Scholar]
D. Zhao, H. Li, On the computation of inverses and determinants of a kind of special matrices. Applied Mathematics and Computation, 250, 721–726 (2015) [CrossRef] [Google Scholar]

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[1] A. Papoulis, S. U. Pillai. Probability, random variables, and stochastic processes. Tata McGraw- Hill Education, (2002) [Google Scholar]

[2] A. Hyvärinen A, P. Pajunen. Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks, 12(3), 429–439 (1999) [Google Scholar]

[3] M. Li, X. Huang, H. Liu, B. Liu, Y. Wu. Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self- adaptive particle swarm optimization and a clustering method. Journal of Applied Polymer Science, 130(5), 3825–3832 (2013) [CrossRef] [Google Scholar]

[4] X. M. Wang. Nonlinear Blind Source Separation using GA optimized RBF-ICA and its Application to the Image Noise Removal. International Conference on Biotechnology, Chemical and Materials Engineering, (2011) [Google Scholar]

[5] T. Kurihara, K. Jinno. Analysis of convergence property of PSO and its application to nonlinear blind source separation. IEEE Congress on Evolutionary, (2013) [Google Scholar]

[6] J. Yang, Z. Zhuang, L. Shi. Multi-universe parallel quantum genetic algorithm. ACTA ELECTRONICA SINICA., 32(6), 923–928 (2004) [Google Scholar]

[7] D. S. Broomhead, D. Lowe. Multivariate functional interpolation and adaptive networks. Complex System, 2, 321–355 (1998) [Google Scholar]

[8] J. A. Yang, Y. Zou, Z. Q. Zhuang. Nonlinear blind source separation algorithm using multi-universe parallel quantum genetic algorithm. Journal of Electronics and Information Technology, 2004, 26(8): 1210–1217. [Google Scholar]

[9] G. Li, K. H. Lee, K. S. Leung, Genetic algorithm based on independent component analysis for global optimization. Parallel Problem Solving from Nature-PPSN IX, Springer Berlin Heidelberg, Iceland, 172–181 (2006) [CrossRef] [Google Scholar]

[10] H. Li, C. Wang, D. Zhao. An Improved EMD and Its Applications to Find the Basis Functions of EMI Signals. Mathematical Problems in Engineering, (2015) [Google Scholar]

[11] H. Li, Z. Song, D. Zhao, P. Wang, J. Chen. A Single Channel EMI Signal Separation Method Based on Directly-mean Empirical Mode Decomposition. Journal of Infromation … Computation Science, 12(17), 6333–6340 (2015) [CrossRef] [Google Scholar]

[12] D. Zhao, H. Li, On the computation of inverses and determinants of a kind of special matrices. Applied Mathematics and Computation, 250, 721–726 (2015) [CrossRef] [Google Scholar]