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ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

March 2014, Vol. 26, No. 3, Pages 592-610
(doi: 10.1162/NECO_a_00554)
© 2014 Massachusetts Institute of Technology
A Parallel Dual Matrix Method for Blind Signal Separation
Article PDF (955.81 KB)
Abstract

A parallel dual matrix method that considers all cases of numerical relations between a mixing matrix and a separating matrix is proposed in this letter. Different constrained terms are used to construct cost function for every subalgorithm. These constrained terms reflect numerical relation. Therefore, a number of undesired solutions are excluded, the search region is reduced, and the convergence efficiency of the algorithm is ultimately improved. Moreover, any parallel subalgorithm is proven to converge to a desired separating matrix only if its cost function converges to zero. Computer simulations indicate that the algorithm efficiently performs blind signal separation.