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The Smagorinsky model formulation is used to model the unknown turbulent stresses.
In the model, the unknown source amplitudes of the virtual sources are adaptively estimated by using Kalman filters (KFs).
A new dynamic neural network structure is utilised to model the unknown plant dynamics through modelling the input-output mapping.
The adaptive filter, which is chosen to be of the same length as, will model the unknown impulse response using the near-end signal (2).
By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations.
Fuzzy logic systems (FLSs) combined with adaptive backstepping method are applied to model the unknown nonlinear functions of the switched system.
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In this case, the process noise wk−1 models the unknown movements along x and y axes.
For the classic optimal control problem, GPDP models the unknown value functions with Gaussian processes and generalizes dynamic programming to continuous-valued states and actions.
A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes.
J. Meng et al. propose a novel Bayesian sparse-correlated rectified factor model (BSCRFM) that models the unknown transcription factor (TF) protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes.
Under the assumption that mismatched unknown inputs are deterministic and can be expressed as the responses of fictitious autonomous dynamical systems, an augmented system is obtained by combining the original system model with the unknown input model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com