Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for Inverse Problems
| Autor: | I. G. Rosen, Susan E. Luczak, Jian Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Blind deconvolution
Applied Mathematics Time series approach System identification 030508 substance abuse Distributed parameter model Inverse problem Article 03 medical and health sciences 0302 clinical medicine Distributed parameter system Frequency domain 030212 general & internal medicine Deconvolution 0305 other medical science Algorithm Mathematics |
| Zdroj: | J Inverse Ill Posed Probl |
| ISSN: | 1569-3945 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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