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In the next sections we present the techniques that we use to improve model reduction performance.
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However, the approximated error between the model and the real process due to model reduction might bring significant performance degradation.
The solution of corresponding model reduction problem with pre-specified performance is given by using the projection Lemma.
At the same time, in dense domains, the overhead of reachability analysis does not affect the general performance of model reduction because we use efficient ADD operations to find the reachable states.
In this work, we thus offer a technique for controlled performance-driven model reduction of GSPNs, using structural simplification rules, namely foldings.
Such a kind of model reduction not only guarantee the FD performance, but also reduce the system dimensions.
Experimental results proved the feasibility of the model in predicting the friction drag reduction performance of the structure.
A coupled piezo-structural-acoustic finite element model is developed to evaluate the noise reduction performance.
The model reduction will thus have little influence on the control performance in terms of output deviation after a long enough time.
Besides the techniques applied for hybrid modelling and parameter estimation, the paper also discusses the techniques needed for model reduction, model tracking and state estimation to make the high performance model based applications work properly.
This article presents a new approach for model reduction of continuous-time uncertain linear systems using the H2 norm as performance criterion.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com