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Moreover, both nonlinear moving average models and NARMAX ones have been designed.
Under statistical and deterministic formulations, we begin with autoregressive and moving average models and study both the batch and recursive formulations of these problems.
Thus, as in the "Local average models" and "Local aggregate model" sections above, the individual error term, εi,g is assumed to be mean independent of node- and network-level observable characteristics and the network.
A new application of the Proper Orthogonal Decomposition (POD) is tested from two different databases: (i) wall pressure fields synthesized from theoretical average models and (ii) wall pressure fields obtained from Lattice Boltzmann Method numerical simulation.
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The required window function is estimated based on the autoregressive moving average modeling and Karhunen Loeve expansion.
Both the time average model and time slice model were used to determine direct and indirect heat recovery potentials.
The average modelling and forecasting error in Fig. 2a are 20.32 and 26.46% respectively, so the average random error is 6.13%.
In fact, well-represented contextual factors contribute to generate the average model, and rare contextual questions are taken into account for the transforms.
We present in Figure 4 MF and SV spectra at the Earth's surface for epochs 2010, 2015, and 2020, for both the average model and the estimated model errors (derived from the posterior covariance matrix).
The mid lower crust in the plateau is relatively slow compared to the average model and Sichuan Basin and Poisson's ratio is anomalous (exceeding 0.3), which is consistent with weak mid lower crust in the plateau.
Ten independent reconstructions yielded reproducible models and the average model and the most probable model are displayed in figure 3C.
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