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Making model based optimal decisions under uncertainty is therefore a challenging task.
MOOSE is a model based optimal input design toolbox developed for Matlab.
A computational framework is presented for model based optimal design of chemical reactors and applied to a multistage ethylene polymerization process.
When model based optimal experimental design techniqes cannot be applied, the extraction of informative segements from historical data can also support system identification.
These parameter values can be hard to estimate exactly (e.g., thermal conductivity) or vary over time (e.g., due to fouling) potentially leading to hazardous situations when applying a model based optimal solution.
The traditional Model Based Optimal Experimental Design (MBOED) techniques focus on improving the parameter precision but do not consider the undesired possibility of increasing the correlation among the estimated parameters.
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Eventually, five predictive models based optimal parameters selection were successfully constructed.
This paper presents an efficient multi-objective model based optimization approach for the optimal sizing of all components and the determination of the best power electronic layout.
A general branch flow model based relaxed optimal power flow (BFM-ROPF) model is then formulated as a SOCP problem after conic relaxation.
We present a model based on optimal control theory representing major tradeoffs that exist between a local design strategy and a global design strategy.
Building on sociological data, we propose a mathematical model based on optimal control theory accounting for the rational planning by a homogamous couple of a long term relationship.
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