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The original methodology uses an iterative three-stage modeling approach: Model identification and model selection.
The algorithms are then applied to engineering and macroeconomic modelling problems, including designing robust optimally informative experiments for dynamic model identification and verification, and robust macroeconomic policies for inflation targeting.
The approach integrates process analysis, model identification, and process synthesis.
Model identification and discrimination are two major statistical challenges.
Teams work along various stages of projects, considering needs assessment, technology and business model identification, and implementation strategies.
The role of data prefiltering in model identification and validation is presented in this paper.
Important issues concerning inherent model identification and optimal control computation are briefly discussed.
This paper deals with the application of model identification and control to an industrial polymerisation process.
Next, model analysis, model identification and model validation based on available reactor operational data are performed.
The performance of a Kalman filter depends on the proper model identification and parameter estimation.
Model identification and validation are based on clinical data from closed-loop experiments.
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