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Identification of parametric models : from experimental data

Eric Walter, +1 more
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The article was published on 1997-01-01 and is currently open access. It has received 1251 citations till now. The article focuses on the topics: Parametric model & Experimental data.

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"Population" approach improves parameter estimation of kinetic models from dynamic PET data

TL;DR: Simulated and real case studies results show that a population approach is of potential in modeling PET images since it allows to reliably estimate model parameters also in those ROIs where either a bad SNR or a poor sampling make the use of LS unsuccessful.
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Sensor network scheduling for identification of spatially distributed processes

TL;DR: The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values.
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Good modeling practice for industrial chromatography: Mechanistic modeling of ion exchange chromatography of a bispecific antibody

TL;DR: This study underlines the need of a standardized methodology for mechanistic model calibration in the biopharmaceutical industry by applying a lumped rate model to the polishing chromatography of a bispecific antibody.
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Optimal experiment design under process noise using riccati differential equations

TL;DR: To optimize an approximation of the predicted variance–covariance matrix of the parameter estimates, which can be computed as the solution of a Riccati differential equation, is suggested.
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Configuring A Sensor Network for Fault Detection in Distributed Parameter Systems

TL;DR: A relaxed problem is discussed in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gauged sites, and a gradient projection algorithm is proposed to perform the search for the optimal solution.