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Optimal inference with chaotic dynamicsNonlinear mappings that exhibit chaotic, seemingly random, evolution have appeal as models of dynamic systems. Their deterministic evolution, vis-a-vis Markov evolutions, results in much simpler optimal detection and estimation algorithms. The variation of a chaotic parameter (mu) results in diverse evolutions, suggesting a simple but rich source of model variations. For the specific mapping examined, this latter possibility is problematic due to the extreme sensitivity on mu of the evolution in the chaotic regime.
Document ID
19840008807
Acquisition Source
Legacy CDMS
Document Type
Other
Authors
Harger, R. O.
(Maryland Univ. College Park, MD, United States)
Date Acquired
August 12, 2013
Publication Date
December 31, 1983
Publication Information
Publication: Sea Height Inform. from Complex SAR Data
Subject Category
Numerical Analysis
Accession Number
84N16875
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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