Independent component analysis based on quantum particle swarm optimization

Abbas, Nidaa A.; Salman, Hussein

doi:10.1016/j.eij.2017.11.001

Cited by 9 publications

(14 citation statements)

References 3 publications

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“…Where ( ) = [ 1 , 2 , … , ] represents × 1 observations vector, ( ) = [ 1 , 2 , … , ] is a × 1 unknown source vector and zero-mean non-Gaussian elements , and is an unknown × nonsingular mixing matrix. Above model is the general linear model of the ICA methods [2], [3], [7].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…Where represents the -th cumulant, is an expectation operation, and is data vector of the signals [1], [7], [14], [15].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…Where ( ) calculated using Monte Carlo estimation approach, where denote a standard deviation, also the particle position can be calculated as in (7).…”

Section: Quantum Particle Swarm Optimization (Qpso)mentioning

confidence: 99%

“…Salman and Abbas [7] introduced new method to optimize the ICA method by using quantum particle swarm optimization method. This method used a Negentropy function as an objective function in the ICA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of the proposed method compared with other methods as the FastICA [5], [6], and "the ICA based on quantum particle swarm optimization" as in [7]. In addition, evaluate the proposed method by number of measurements as the signal-to-noise ratio (SNR) [8], the signal-to-distortion ratio (SDR) [9], the absolute value of correlation coefficient (AVCC) [10], and the computation time.…”

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confidence: 99%

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Bigradient neural network-based quantum particle swarm optimization for blind source separation

Salman

Al-Qurabat

Finjan

2021

IJ-AI

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An independent component analysis (ICA) is one of the solutions of a blind source separation problem. ICA is a statistical approach that depends on the statistical properties of the mixed signals. The purpose of the ICA method is to demix the mixed source signals (observation signals) and rcovering those signals. The abbreviation of the problem is that the ICA needs for optimizing by using one of the optimization approaches as swarm intelligent, neural neworks, and genetic algorithms. This paper presents a hybrid method to optimize the ICA method by using the quantum particle swarm optimization method (QPSO) to optimize the Bigradient neural network method that applies to separate mixed signals and recover sources signals. The results of an implement this work prove that this method gave good results comparing with other methods such as the Bigradient neural network and the QPSO method, based on several evaluation measures as signal-to-noise ratio, signal-to-distortion ratio, absolute value correlation coefficient, and the computation time.

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Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…Where represents the -th cumulant, is an expectation operation, and is data vector of the signals [1], [7], [14], [15].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…Where ( ) calculated using Monte Carlo estimation approach, where denote a standard deviation, also the particle position can be calculated as in (7).…”

Section: Quantum Particle Swarm Optimization (Qpso)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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