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A new blind source separation method based on fractional lower‐order statistics
Author(s) -
Zha Daifeng,
Qiu Tianshuang
Publication year - 2006
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.896
Subject(s) - stable distribution , blind signal separation , stability (learning theory) , higher order statistics , noise (video) , algorithm , convergence (economics) , alpha (finance) , computer science , order statistic , artificial neural network , gaussian , mathematics , statistics , mathematical optimization , artificial intelligence , machine learning , signal processing , telecommunications , psychometrics , channel (broadcasting) , radar , construct validity , physics , quantum mechanics , economics , image (mathematics) , economic growth
We proposed neural network structures related to multilayer feed‐forward networks for performing blind source separation (BSS) based on fractional lower‐order statistics. As alpha stable distribution process has no its second‐ or higher‐order statistics, we modified conventional BSS algorithms so that their capabilities are greatly improved under both Gaussian and lower‐order alpha stable distribution noise environments. We analysed the performances of the new algorithm, including the stability and convergence performance. The analysis is based on the assumption that the additive noise can be modelled as alpha stable process. The simulation experiments and analysis show that the proposed class of networks and algorithms is more robust than second‐order‐statistics‐based algorithm. Copyright © 2006 John Wiley & Sons, Ltd.