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Confidence measure estimation in dynamical systems model input set selection

  • Paul B. Deignan
  • , Galen B. King
  • , Peter H. Meckl
  • , Kristofer Jennings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An information-theoretic input selection method for dynamical system modeling is presented that qualifies the rejection of irrelevant inputs from a candidate input set with an estimate of a measure of confidence given only finite data. To this end, we introduce a method of determining the spatial interval of dependency in the context of the modeling problem for bootstrap mutual information estimates on dependent time-series. Additionally, details are presented for determining an optimal binning interval for histogram-based mutual information estimates.

Original languageEnglish (US)
Title of host publicationProceedings of the 2004 American Control Conference (AAC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2824-2829
Number of pages6
ISBN (Print)0780383354
DOIs
StatePublished - 2004
Externally publishedYes
Event2004 American Control Conference, AAC 2004 - Boston, MA, United States
Duration: Jun 30 2004Jul 2 2004

Publication series

NameProceedings of the American Control Conference
Volume3
ISSN (Print)0743-1619

Conference

Conference2004 American Control Conference, AAC 2004
Country/TerritoryUnited States
CityBoston, MA
Period6/30/047/2/04

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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