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Based on the computational results, surrogate-based weighted average models are constructed and the population based multi-objective evolutionary algorithm gave Pareto optimal solutions.
The annual median value of ECI in the three cities is about 36 kWh/m2.Hourly energy consumption prediction model based on data mining (ie. ANN (Artificial Neural Network), SVM (Support vector regression) and ARIMA (Autoregressive integrated moving average) models) are subsequently discussed.
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Average models were successfully implemented into FORTRAN routines and used to simulate capsule deployment with a finite element method analysis.
Single, double, centered and weighted moving average models were tested for the available data with different orders and intervals.
Results from unconstrained distributed lag models and 4-day moving average models were similar [see Supplemental Material, Table S2 (http://dx.doi.org/10.1289/ehp.1104349)].
Following Penner et al. [ 125] a total of 100 ENMs were calculated and replicated using sub-sampling (70% model training and 30% model testing) and finally three average models were derived: maximum, mean and minimum prediction gained.
By considering the unmodeled dynamics of the SC and the unknown disturbances, the equivalent circuit and the state-space average model are established for the boost converter based battery/SC HESS.
The accuracy, convergence and the region of validity of the averaged models are examined for some special cases.
However, that virtue comes at a cost: The experimental paradigms and the averaging models are specific to estimating a scalar parameter of the target.
The state-space average model is established for the battery/SC HESS.
A weighted average model was then used to digitally map soil texture.
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