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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.
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.
Also this has potential application when it is hard to find a mathematical model from experimental data to describe the internal force vector for a truss, as well as identification and control of trusses where precise modeling of the structure is a key point to the success of the application.
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 identification of a nonlinear dynamic model from experimental observations is one of the most challenging system identification problems.
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We propose deriving wireless simulation models from experimental traces of radio signal strength.
A systematic approach for developing phenomena models from experimental data is presented.
This work also established a general method to develop stochastic models from experimental information.
Parameter identifiability constitutes an important issue for development of quantitative dynamic process models from experimental data.
However, the determination of the isotherms typically requires the identification of unknown parameters in postulated models from experimental data.
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