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We viewed reverse dosimetry as a type of model calibration problem where, using an otherwise calibrated model, we determined unmeasured exposure parameters based on the measured biomonitoring data.
The model calibration problem can be formulated as a non-linear optimization problem.
Importantly, it is a general purpose framework that can be easily applied to any model calibration problem.
The model calibration problem is solved for each replicate and the cloud of solutions is recorded in a matrix.
The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem.
The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account.
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To deal with first difficulty several authors have proposed the use of global optimization methods [ 31- 34], since most of the model calibration problems related to biological models have several sub-optimal solutions.
Using an alternating directions optimization algorithm, the regularized objective function is divided into a parameterized model calibration sub-problem, which is solved via gradient-based optimization.
Unlike existing calibration models, the proposed model makes the calibration problem well-posed.
Although E-PLS has been designed with batch processes in mind, it could potentially serve to obtain better models for calibration problems with short and fat data sets.
A new calibration model by formulating the calibration problem as a multi-objective optimization problem is presented.
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