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The proposition is based on an architecture integrating the control system with the virtual controlled system (emulation model), and on generic constructs to build this emulation model.
This revealed sufficient approximation of the digital emulation model by the physical translation (see Fig. 9).
The obtained nominal emulation model was tested for four floodplains along the river Dender in Belgium.
The final emulation model is then identified using Extremely Randomized Trees.
The envisaged IP-RAN emulation model takes into account losses in the transport network, obtained from non-real-time simulations, as shown in Figure 6.
This provided sufficient confidence in our digital emulation model and implementation methodology to continue with the final step of full scale construction.
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As the number and forms of the problem that benefit from the identification and subsequent use of an emulator is very large, emulation modelling has emerged in different sectors of science, engineering and social science.
The main aim of the paper is to provide an introduction to emulation modelling, together with a unified strategy for its application, so that modellers from different disciplines can better appreciate how it may be applied in their area of expertise.
In this paper we propose a novel data-driven Dynamic Emulation Modelling approach for the construction of small, computationally efficient models that accurately emulate the main dynamics of the original process-based model, but with less computational requirements.
Emulation modelling is an effective way of overcoming the large computational burden associated with the process-based models traditionally adopted by the environmental modelling community.
Particular emphasis is devoted to Dynamic Emulation Modelling (DEMo), a methodological approach that preserves the dynamic nature of the original process-based model, with consequent advantages in a wide variety of problem areas.
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