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Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods.
This paper presents a methodology for model based robust fault diagnosis and a methodology for input design to obtain optimal diagnosis of faults.
A Bayesian probabilistic methodology for model updating is first implemented for the purpose of updating the structural model using dynamic data.
The paper presents the methodology for model setup and the simulation results for the main water balance components of the catchment: total runoff at several gauging stations, runoff components, evapotranspiration and soil moisture.
The overall methodology for model development is depicted in Fig. 1.
Also, Momma and Bennett (2002) developed a fully automated pattern search (PS) methodology for model selection of SVR.
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We combine these steps into an explicit and holistic methodology for model-based design of cyber-physical systems from abstraction to architecture, and from concept to realization.
It then presents essential methodology for model-guided analyses of biological data, covering such methods as network reconstruction, uncertainty quantification, and experimental design; practical algorithms and software packages for modeling biological systems; and specific examples of current quantitative biology research and related specialized methods.
A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain.
In addition to catalysis by metal surfaces, ReaxFF is also an effective methodology for modelling the catalytic properties of oxides.
Defining a best practice methodology for modeling the environmental performance of agriculture.
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