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• W2738906282 endingPage "114" @default.
• W2738906282 startingPage "89" @default.
• W2738906282 abstract "The first part is concerned with whether the parameters of a model can be identified (uniquely or with several solutions) from a specified input-output experiment if perfect data are available. For linear, time-invariant models, there are several approaches available for this aspect of identifiability analysis, alternatively referred to as structural, deterministic or a priori identifiabi1ity analysis, and five such approaches are described. Only one, based on the Taylor series expansion of the observations, is directly applicable to nonlinear models or tine-varying systems. It is illustrated by analysing two nonlinear models When a model is unidentifiable from a proposed experiment, physically based constraints on the model often provide a means for computing finite bounds for the parameters (interval identifiability). This is illustrated for the class of linear, time-invariant models in which the system matrix is of compartmental form The identifiability question in the presence of real, noisy data is considered last. In the context of parameter estimation, this aspect of the analysis, often referred to in the literature as numerical or a posteriori identifiabi1ity analysis, is essentially the problem of parameter estimation accuracy, given that the parameters are known to be structurally identifiable Many of the issues discussed throughout the paper are illustrated by two examples drawn from the literature, the first a linearised model of ship steering dynamics and the second a nonlinear model for a microbial growth process" @default.
• W2738906282 created "2017-07-31" @default.
• W2738906282 creator A5001710170 @default.
• W2738906282 creator A5019845182 @default.
• W2738906282 date "1985-07-01" @default.
• W2738906282 modified "2023-10-16" @default.
• W2738906282 title "Identifiability of Model Parameter" @default.
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• W2738906282 doi "https://doi.org/10.1016/s1474-6670(17)60544-5" @default.
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