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 articlepeer-review

    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

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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