Confidence measure estimation in dynamical systems model input set selection

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

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 journal › Conference article › peer-review

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 language	English (US)
Pages (from-to)	2824-2829
Number of pages	6
Journal	Proceedings of the American Control Conference
Volume	3
State	Published - 2004
Externally published	Yes
Event	Proceedings of the 2004 American Control Conference (AAC) - Boston, MA, United States Duration: Jun 30 2004 → Jul 2 2004

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "Confidence measure estimation in dynamical systems model input set selection",

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.",

author = "Deignan, {Paul B.} and King, {Galen B.} and Meckl, {Peter H.} and Kristofer Jennings",

year = "2004",

language = "English (US)",

volume = "3",

pages = "2824--2829",

journal = "Proceedings of the American Control Conference",

issn = "0743-1619",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Confidence measure estimation in dynamical systems model input set selection

AU - Deignan, Paul B.

AU - King, Galen B.

AU - Meckl, Peter H.

AU - Jennings, Kristofer

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

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M3 - Conference article

AN - SCOPUS:8744302819

SN - 0743-1619

VL - 3

SP - 2824

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JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

T2 - Proceedings of the 2004 American Control Conference (AAC)

Y2 - 30 June 2004 through 2 July 2004

ER -

Confidence measure estimation in dynamical systems model input set selection

Abstract

ASJC Scopus subject areas

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