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PARAMETER IDENTIFIABILITY THROUGH INFORMATION THEORY
2017
Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
unpublished
In this paper, we address the problem of assessing the identifiability of model parameters in a mechanical system, i.e., whether unknown parameters can be estimated given a set of measurements collected through sensor networks. Practical identifiability can arise due to either a lack of sensitivity or a joint e ect of the parameters on the measurements. Information theory can be used to detect the sources of non-identifiability, with the purpose of establishing an e cient sensor network design.
doi:10.7712/120217.5376.17179
fatcat:yvfhngkl3bgcfbkhep7vvg4fqy