Confidence measure estimation in dynamical systems model input set selection

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

Research output: Contribution to journalConference article

1 Scopus citations

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)
Pages (from-to)2824-2829
Number of pages6
JournalProceedings of the American Control Conference
Volume3
StatePublished - Nov 29 2004
EventProceedings of the 2004 American Control Conference (AAC) - Boston, MA, United States
Duration: Jun 30 2004Jul 2 2004

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Deignan, P. B., King, G. B., Meckl, P. H., & Jennings, K. (2004). Confidence measure estimation in dynamical systems model input set selection. Proceedings of the American Control Conference, 3, 2824-2829.