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Quantifying self-organization with optimal wavelets
dc.creator | Milovanović, Miloš | |
dc.creator | Rajković, Milan | |
dc.date.accessioned | 2018-03-01T23:31:48Z | |
dc.date.available | 2018-03-01T23:31:48Z | |
dc.date.issued | 2013 | |
dc.identifier.issn | 0295-5075 | |
dc.identifier.issn | 1286-4854 | |
dc.identifier.uri | https://vinar.vin.bg.ac.rs/handle/123456789/5569 | |
dc.description.abstract | An 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 selforganization 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 systems dynamics. The causal states and the wavelet machine (w-machine) are defined in analogy with the epsilon-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. Copyright (C) EPLA, 2013 | en |
dc.relation | info:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/174014/RS// | |
dc.relation | info:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/44006/RS// | |
dc.rights | restrictedAccess | en |
dc.source | Europhysics Letters / EPL | en |
dc.title | Quantifying self-organization with optimal wavelets | en |
dc.type | article | en |
dcterms.abstract | Миловановиц, Милос; Рајковић Милан; | |
dc.citation.volume | 102 | |
dc.citation.issue | 4 | |
dc.identifier.wos | 000321118600005 | |
dc.identifier.doi | 10.1209/0295-5075/102/40004 | |
dc.citation.other | Article Number: 40004 | |
dc.citation.rank | M21 | |
dc.identifier.scopus | 2-s2.0-84880521324 |
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