Blind Source Separation Of Post-nonlinear Mixtures Using Evolutionary Computation And Order Statistics

dc.creatorDuarte L.T.
dc.creatorSuyama R.
dc.creatorAttux R.R.D.F.
dc.creatorVon Zuben F.J.
dc.creatorRomano J.M.T.
dc.date2006
dc.date2015-06-30T18:12:40Z
dc.date2015-11-26T14:27:28Z
dc.date2015-06-30T18:12:40Z
dc.date2015-11-26T14:27:28Z
dc.date.accessioned2018-03-28T21:30:36Z
dc.date.available2018-03-28T21:30:36Z
dc.identifier3540326308; 9783540326304
dc.identifierLecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 3889 LNCS, n. , p. 66 - 73, 2006.
dc.identifier3029743
dc.identifier10.1007/11679363_9
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33745725572&partnerID=40&md5=a69a143818feb9ab46b8af5fd0582c75
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/103511
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/103511
dc.identifier2-s2.0-33745725572
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1246347
dc.descriptionIn this work, we address the problem of source separation of post-nonlinear mixtures based on mutual information minimization. There are two main problems related to the training of separating systems in this case: the requirement of entropy estimation and the risk of local convergence. In order to overcome both difficulties, we propose a training paradigm based on entropy estimation through order statistics and on an evolutionary-based learning algorithm. Simulation results indicate the validity of the novel approach. © Springer-Verlag Berlin Heidelberg 2006.
dc.description3889 LNCS
dc.description
dc.description66
dc.description73
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dc.descriptionAttux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal on Applied Signal Processing Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis, 2003 (8), pp. 740-747
dc.descriptionDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Trans. on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), pp. 239-251
dc.languageen
dc.publisher
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsfechado
dc.sourceScopus
dc.titleBlind Source Separation Of Post-nonlinear Mixtures Using Evolutionary Computation And Order Statistics
dc.typeActas de congresos


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