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dc.contributor.authorMilovanović, Milošen
dc.contributor.authorRajković, Milanen
dc.date.accessioned2020-05-01T20:12:38Z-
dc.date.available2020-05-01T20:12:38Z-
dc.date.issued2013-05-01en
dc.identifier.issn0295-5075en
dc.description.abstractAn optimal wavelet basis is used to develop a quantitative, experimentally applicable criterion for self-organization. The choice of the optimal wavelet is based on the model of self-organization in the wavelet tree. The framework of the model is founded on the wavelet-domain hidden Markov model and the optimal wavelet basis criterion for self-organization. The principle assumes increase in statistical complexity considered as the information content necessary for maximally accurate prediction of the system's dynamics. The causal states and the wavelet machine (w-machine) are defined in analogy with the -machine constructed as the unique, minimal, predictive model of the process. The method, presented here for the one-dimensional data, concurrently performs superior denoising and may be easily generalized to higher dimensions.en
dc.publisherIOP Science-
dc.relation.ispartofEPLen
dc.titleQuantifying self-organization with optimal waveletsen
dc.typeArticleen
dc.identifier.doi10.1209/0295-5075/102/40004en
dc.identifier.scopus2-s2.0-84880521324en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.issue4en
dc.relation.volume102en
dc.description.rankM21-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
crisitem.author.orcid0000-0002-2909-451X-
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