[1]ZHOU Shuanghong,WANG Lingling.Research on multi-eigenvalue decomposition blind source separation algorithm for sparse chaotic signals[J].CAAI Transactions on Intelligent Systems,2018,13(5):843-847.[doi:10.11992/tis.201703032]
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Research on multi-eigenvalue decomposition blind source separation algorithm for sparse chaotic signals
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 5
Page number:
843-847
Column:
学术论文—机器学习
Public date:
2018-09-05
- Title:
- Research on multi-eigenvalue decomposition blind source separation algorithm for sparse chaotic signals
- Keywords:
- chaotic signals; blind source separation; phase space; separation matrix; particle swarm optimization; multi-eigenvalue decomposition; minimum mutual information method; maximum likelihood estimation; independent component analysis
- CLC:
- TP181
- DOI:
- 10.11992/tis.201703032
- Abstract:
- To perform high-precision restructuring of chaotic laser-source signals that are experiencing noise interference, in this paper, we propose a blind-source-separation algorithm based on a phase-space-reconstructed chaotic stream signal. This algorithm first performs a time-delay reconstruction of the phase space of separation signals, and then treats the separation matrix as a parameter to be optimized. Then, it converts the blind source separation into an optimization problem by constructing an objective function in the phase space, and solves the optimal separation matrix using a particle swarm optimization algorithm. It then multiplies the observation data by the optimal separation matrix to reconstruct the source signals. Experimental results show that the algorithm achieves rapid convergence, and its accuracy is obviously superior to the existing independent component analysis method under various noise intensities.
- References:
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Last Update:
2018-10-25