Blind Source Separation of Post-Nonlinear Mixtures Using Evolutionary Computation and Gaussianization

Autor:	Romis Attux, Tiago M. Dias, João Marcos Travassos Romano, Ricardo Suyama
Rok vydání:	2009
Předmět:	Nonlinear system Mathematical optimization Artificial immune system business.industry Computer science Evolutionary algorithm Source separation Local search (optimization) business Independent component analysis Blind signal separation Evolutionary computation
Zdroj:	Independent Component Analysis and Signal Separation ISBN: 9783642005985 ICA ResearcherID
DOI:	10.1007/978-3-642-00599-2_30
Popis:	In this work, we propose a new method for source separation of post-nonlinear mixtures that combines evolutionary-based global search, gaussianization and a local search step based on FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources, and, as shown by the simulation results, this aim was satisfactorily fulfilled.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92ef461e25df385eefc6910f3f2fe005 https://doi.org/10.1007/978-3-642-00599-2_30 Zobrazit plný text záznamu