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We propose deriving wireless simulation models from experimental traces of radio signal strength.
This work also established a general method to develop stochastic models from experimental information.
A systematic approach for developing phenomena models from experimental data is presented.
Parameter identifiability constitutes an important issue for development of quantitative dynamic process models from experimental data.
Deviation of these models from experimental data is likely due to multiple processes occurring over the time series under investigation.
However, the determination of the isotherms typically requires the identification of unknown parameters in postulated models from experimental data.
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Processing units in the chemical plant can be modeled using rigorous unit models or surrogate models generated from experimental results or off-line calculations.
The paper considers the problem of updating an analytical model from experimental data using the reference basis approach.
A grey-box identification approach of the authors is employed for obtaining the model from experimental data or from detailed Finite Element Model (FEM) simulators.
In [2], the so-called POCA-NAZU model is introduced to describe the small scale multipath fading amplitudes for UWB signals, while [3] derives a STDL propagation model from experimental data.
The obtained model from experimental design revealed that number of cashier and inter arrival time were significant in determining the queue length while all the factors and their interaction were significantly affecting the sales rate.
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