scholarly journals Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Lei Chen ◽  
Liyi Zhang ◽  
Yanju Guo ◽  
Yong Huang ◽  
Jingyi Liang
Keyword(s):  
Blind Source Separation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Source Separation ◽  
Separation Algorithm ◽  
Simulation Experiments ◽  
Bee Colony ◽  
Source Signal ◽  
Source Signals

The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

scholarly journals Constrained Blind Source Separation by Morphological Characteristics and Its Application in Modal Analysis

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Teng Gong ◽  
Zhousuo Zhang ◽  
Huan Wang
Keyword(s):  
Modal Analysis ◽  
Blind Source Separation ◽  
Prior Information ◽  
Source Separation ◽  
Morphological Characteristics ◽  
Separation Algorithm ◽  
Contrast Function ◽  
Optimization Calculation ◽  
Source Signal ◽  
Source Signals

Semi-blind source separation algorithm is widely concerned for its advantages over classical blind source separation algorithm. However, in practical applications, it is often a difficult problem to design reference signals, which should be closely related to the desired source signals. Therefore the algorithm of constrained blind source separation by morphological characteristics is proposed in this paper, including three steps: the establishment of the enhanced contrast function, the optimization calculation and the extraction of multiple source signals. Firstly, the indexes measuring the morphological characteristics of a source signal are constructed based on the known prior information and introduced into the traditional contrast function to establish an enhanced contrast function, extending the use of prior information. Then, the optimization calculation is accomplished by genetic algorithm, obtaining a single source signal. Finally, the extraction of multiple source signals is realized by cluster analysis. The proposed algorithm is applied to the modal analysis under random excitation. The spectrum symmetry index is constructed and introduced into the kurtosis contrast function to establish the enhanced contrast function, thus realizing the extraction of each signal modal response. The extraction results show the effectiveness and superiority of the algorithm.

A New Method for Adaptive Blind Source Separation Based on the Estimated Number of Dynamic Fault Sources

2012 ◽  
Vol 233 ◽  
pp. 211-217 ◽  
Author(s):  
Xiao Yan Yang ◽  
Xiong Zhou ◽  
Yi Ke Tang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Algorithm ◽  
Accurate Estimation ◽  
Separation Algorithm ◽  
Underdetermined Blind Source Separation ◽  
Source Signal ◽  
Fault Source ◽  
Source Number

In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.

Augmented Hardware Design of BSS in Real Time by Real Time Feasible Source Signal Separator

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Algorithm ◽  
Underdetermined Blind Source Separation ◽  
Memory Space ◽  
The Real ◽  
Wide Range ◽  
Source Signal ◽  
Band Limited

Blind source separation is a blooming sector in the digital signal processing for severing exact signal from the dense recorded. Exclusively, among the “Blind Source Separation” the “Under Determined Blind Source Separation” is considered than an over determined Blind Source Separation due to its wide range of usage. Nevertheless, it is seen that the real implementation is very rarely done in existing researches, because the real time implementation of UBSS (Underdetermined Blind Source Separation)is existed to be a challenging one due to its lacking hardware characteristics of increased latency, reduced speed and consumption of more memory space. Consequently, there is an increase need to implement an Underdetermined source signal separation real time with improved hardware utility that in this Unswerving framework a Real time feasible Source Signal separator is formulated in which initially the source signals are decomposed by Boosted band-limited VMD (Variational Mode Decomposition)into the “Multi component Signal” and then to an amount of "Band-Limited” IMF subjected to the Encompassed Hammer sley–Clifford source separation algorithm that uses expectation-maximization and Gibbs sampling an alternative to deterministic algorithms to determine the exact estimated parameter from E-M method. Subsequently, the source separation algorithm infers the best separation of sources signals by exact estimation and determination from the decomposed signals, whereas the iterations in E-M estimation are reduced by Gauss-Seidel method. Thus our novel source signal separator internally with a signal decomposer and a source separation algorithm with lesser number of iterations which reduces memory consumption and yields better hardware realization with reduced latency and increased speed. The proposed Implementation is done in Xilinx Platform.

A Memetic Artificial Bee Colony Algorithm for High Dimensional Problems

2020 ◽  
Vol 19 (01) ◽  
pp. 2050008
Author(s):  
Dongli Jia ◽  
Teng Li ◽  
Yufei Zhang ◽  
Haijiang Wang
Keyword(s):  
Local Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Small Region ◽  
High Dimensional ◽  
Large Space ◽  
Bee Colony ◽  
Local Search Strategy

This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a “shrinking” local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems.

The Research and Simulation of Blind Source Separation Algorithm

2016 ◽  
Vol 8 (3) ◽  
pp. 1-36
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

The Research and Simulation of Blind Source Separation Algorithm

2020 ◽  
pp. 494-531
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

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