Confidence measure estimation in dynamical systems model input set selection

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 in...

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Bibliographic Details
Main Authors: Deignan, P.B., King, G.B., Meckl, P.H., Jennings, K.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Context modeling
Control theory
Control theory. Systems
Diesel engines
Exact sciences and technology
Histograms
Mechanical engineering
Mechanical variables measurement
Modelling and identification
Mutual information
Nonlinear dynamical systems
Optimal control
Probability density function
Online Access:Request full text
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Description
Summary: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.
ISSN:0743-1619
2378-5861
DOI:10.23919/ACC.2004.1383894