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- W2158678087 abstract "We propose an independent component analysis (ICA) algorithm which can separate mixtures of sub- and super- Gaussian source signals with self-adaptive nonlinearities. The ICA algorithem in the framework of natural Riemannian gradient is derived using the parameterized Weibull density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Weibull density model. Computer simulation results confirm the validity and high performance of the proposed algorithm Key words: Independent component analysis, weibull distribution, maximum likelihood, sub- and super- Gaussian, blind signal separation." @default.
- W2158678087 created "2016-06-24" @default.
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- W2158678087 date "2009-05-31" @default.
- W2158678087 modified "2023-09-28" @default.
- W2158678087 title "Blind signal separation using an adaptive weibull distribution" @default.
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- W2158678087 doi "https://doi.org/10.5897/ijps.9000100" @default.
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