Abstract:
System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to esti...View moreMetadata
Abstract:
System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (Expectation-Maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.
Published in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 25-29 August 2015
Date Added to IEEE Xplore: 05 November 2015
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PubMed ID: 26736981