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This approach combines real time physical model identification with adaptive nonlinear dynamic inversion (NDI).
This procedure combines model identification with pre- and post-identification steps to provide reliable operation.
The work-flow efficiently handles the complexity arising from the large number of design parameters and allows for the model identification with minimal number of CFD simulations.
Open image in new window Fig. 1 Application of ANN in the well-testing model identification with the pressure derivative plots as inputs and scores of different reservoir models as outputs of the network (Vaferi et al. 2011).
Thus, despite identification standards [50] and the integration of model identification with experimental design [51], parameter estimation remains challenging.
We considered separately bA2,1 = bA3,2 = bA4,3 (henceforth equal A b-coefficients), bC2,1 = bC3,2 = bC4,3 (equal C b-coefficients), and bE2,1 = bE3,2 = bE4,3 (equal E b-coefficients), and found that each set of constraints resulted in model identification with unconstrained αk and βk (k = 1,2,3).
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An adaptive damping controller design method by integrating online recursive closed-loop subspace model identification (SMI) with model predictive control theory is proposed in this paper.
Hyper-plane-shaped clustering (HPSC) has been proved to be more effective in Takagi Sugeno (T S) fuzzy model identification compared with hyper-sphere-shaped clustering (HSSC).
Sensitivity analysis has also been used to integrate model identification and discrimination with optimal experimental design.
This paper deals with the design of a model identification fuzzy adaptive controller with real-time scaling factors adjustment and the stability analysis of nonlinear distributed parameter systems.
For this proof of concept study, the model identification process was tested with 46 sets of porcine data from five pigs with induced pulmonary embolism [23].
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