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In this paper the nonlinear model identification of combustion engines using operating regime model structures is discussed.
A possible explanation why the distributed model did not perform better than the simpler model structures is the use of limited available spatial information.
Moreover, the specification of model structures is close to graphical object representation which helps defining a model of a system more in terms of its structure rather than its equations.
It is well known that if we intend to use a minimum variance control strategy, which is designed based on a model obtained from an identification experiment, the best experiment which can be performed on the system to determine such a model (subject to output power constraints, or for some specific model structures) is to use the true minimum variance controller.
Goldblatt (2003, section 4.8) argues convincingly that Kripke's adoption of points of evaluation in the model structures is a particularly crucial innovation.
The transparency of models in terms of data sources and model structures is recognised by most authors as a crucial issue for the future deployment of the models.
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Therefore, low-order model structures are investigated.
Based on the organism different model structures are available.
Many widely used model structures are included in this class.
Complex configurable models of embedded software systems are hard to design and maintain, especially when model structures are variable and the number of allowable configurations is unlimited.
The model structures were assumed to have failed when either of the two limit states occurred.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com