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In particular, it contextualizes and provides a possible biological implementation for models based on maximizing expected value, such as model-based or hierarchical reinforcement learning (Ribas-Fernandes et al. 2011; Huys et al. 2012).
The same approach can be used for models based on streamtubes.
A truncation error analysis is performed for models based on the lattice Boltzmann (LB) equation.
This pattern of very similar coefficients also follows for models based on data transformed at both levels.
Almost all methods have shown overall accuracies higher than 85% instead of 80.8% for models based on physicochemical parameters.
The performance of this new model (R2 = 0.930) is comparable to the one reported for models based on more numerous and more sophisticated predictors.
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Ten variables were selected for modeling based on Pearson's correlation indices obtained from paired analyses.
This model is then used for model based control using internal model control (IMC).
Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control.
The global valid nonlinear model was used for model based predictive control.
Subsequently, the extended Kalman filter algorithm is employed for model based state-of-power prediction.
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