Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for Inverse Problems

Jian Li; Susan E Luczak; I G Rosen

doi:10.1515/jiip-2018-0006

Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for Inverse Problems

J Inverse Ill Posed Probl. 2019 Oct;27(5):703-717. doi: 10.1515/jiip-2018-0006. Epub 2019 Feb 16.

Authors

Jian Li¹, Susan E Luczak², I G Rosen³

Affiliations

¹ Department of Electrical Engineering Systems, University of Southern California, 3740 McClintock Ave. EEB 424, Los Angeles 90089, USA.
² Department of Psychology, University of Southern California, USA.
³ Modeling and Simulation Laboratory, Department of Mathematics, University of Southern California, USA.

Abstract

Three methods for the estimation of blood or breath alcohol concentration (BAC/BrAC) from biosensor measured transdermal alcohol concentration (TAC) are evaluated and compared. Specifically, we consider a system identification/quasi-blind deconvolution scheme based on a distributed parameter model with unbounded input and output for ethanol transport in the skin and compare it to two more conventional system identification and filtering/deconvolution techniques for ill-posed inverse problems, one based on frequency domain methods, and the other on a time series approach using an ARMA input/output model. Our basis for comparison are five statistical measures of interest to alcohol researchers and clinicians: peak BAC/BrAC, time of peak BAC/BrAC, the ascending and descending slopes of the BAC/BrAC curve, and the area underneath the BAC/BrAC curve.

Keywords: 35K90; 47D06; 65M32; 92C55; 93B30; 93C20; Blind deconvolution; Distributed parameter systems; Filtering; See www.ams.org/msc; System identification; Transdermal alcohol biosensor.

Abstract

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