Neural Comput - A parallel dual matrix method for blind signal separation.

Tópicos

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{ take(945) account(800) differ(722) }
{ algorithm(1844) comput(1787) effici(935) }
{ cost(1906) reduc(1198) effect(832) }
{ analysi(2126) use(1163) compon(1037) }
{ model(3480) simul(1196) paramet(876) }
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Resumo

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.

Resumo Limpo

parallel dual matrix method consid case numer relat mix matrix separ matrix propos letter differ constrain term use construct cost function everi subalgorithm constrain term reflect numer relat therefor number undesir solut exclud search region reduc converg effici algorithm ultim improv moreov parallel subalgorithm proven converg desir separ matrix cost function converg zero comput simul indic algorithm effici perform blind signal separ

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