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Independent component analysis based on quantum particle swarm optimization

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 حسين محمد سلمان الشمري
21/03/2019 08:03:02
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A Blind Source Separation (BSS) is one of the DSP which aims to
estimate a set of latent source signals using that is a set of available
statistical properties about these signals. The BSS appeared in the
1980s then expanded rapidly. There are many books describe the
BSS in details as [1–3].
In the BSS, multiple signals are obtained by an array of sensors
and processed in order to recover the initial multiple source signals.
It assumes that the observed data was generated by interactions
between latent variables.
The most commonly mechanism for analyzing latent data is
Independent Component Analysis (ICA). ICA is a probabilistic and
statistical method for separating a multivariate signal into additive
subcomponents supposes the mutual statistical independence of
the non-Gaussian signals of the sources. ICA methods use one of
two properties: Non-Gaussianity or sample dependence [1,2].
The independence assumption is correct in the most cases, so,
the blind, ICA, separation of mixed signals gives very good results.
The methods, that use the statistical properties of the signals, it
will find the independent components by minimizing the statistical
dependence of the estimated signal factors (components).
Non-Gaussianity feature used to measure the independence of
the component, by the kurtosis measurement or approximation
of negentropy

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