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A method of independent component analysis based on radial basis function networks using noise estimation
Author(s) -
Zhang Nuo,
Lu Jianming,
Yahagi Takashi
Publication year - 2008
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10073
Subject(s) - independent component analysis , noise reduction , noise (video) , interference (communication) , nonlinear system , reduction (mathematics) , computer science , radial basis function network , radial basis function , algorithm , signal (programming language) , noise measurement , component (thermodynamics) , artificial intelligence , pattern recognition (psychology) , mathematics , artificial neural network , telecommunications , channel (broadcasting) , physics , geometry , quantum mechanics , image (mathematics) , thermodynamics , programming language
This paper proposes a robust independent component analysis (ICA) approach for noise reduction. Noise reduction is a difficult problem in ICA model. In general signal processing applications, there is more than one interference signal which may have unknown characteristics. In these situations, traditional linear ICA may lead to poor results. Hence, noise reduction is preferred to be performed with nonlinear adaptive filtering. In this paper, a radial basis function network (RBFN) is employed to transform the observed signals into output space in a nonlinear manner. The weights of RBFN are updated by utilizing a modified fixed‐point algorithm. The proposed method has not only the capacity of recovering the mixed signals, but also reducing noise with unknown characteristics from observed signals. The simulation results and analysis show that the proposed algorithm is suitable for practical unsupervised noise reduction problem. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(3): 45–52, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecj.10073