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Communications with chaotic time series: probabilistic methods for noise reduction
Author(s) -
Dedieu Hervé,
Kisel Andrey
Publication year - 1999
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/(sici)1097-007x(199911/12)27:6<577::aid-cta84>3.0.co;2-j
Subject(s) - chaotic , a priori and a posteriori , noise (video) , computer science , probabilistic logic , series (stratigraphy) , noise reduction , implementation , reduction (mathematics) , line (geometry) , algorithm , computer engineering , mathematics , artificial intelligence , paleontology , philosophy , geometry , epistemology , image (mathematics) , biology , programming language
The paper deals with noise decontamination of chaotic time series under the assumption that some a priori information about the system which produced the time series is known in advance. We show that this a priori information can be quite naturally used in standard maximum likelihood approaches. Focusing on low complexity implementations we derive different quasioptimal maximum‐likelihood solutions aimed at off‐line and on‐line noise cleaning. The obtained results show attractive capabilities for on‐line and low‐cost implementation. Copyright © 1999 John Wiley & Sons, Ltd.