Sentence examples for model estimation scheme from inspiring English sources

The model estimation scheme also provides interval estimates of the smooth terms which does not incur any additional simulations.

The first part of the paper presents the chosen model parametrization along with a description of the model estimation scheme and an exploration of various regularization methods.

Subsequently, the batch process model parameters computed via the proposed R2R model parameter estimation scheme are used in a model predictive controller (MPC) within each batch to compute a set of optimal jacket temperatures for the production of crystals with a desired shape distribution in a batch crystallization process.

In this work, we develop a run-to-run (R2R) model parameter estimation scheme based on moving horizon estimation (MHE) concepts for the modeling of batch-to-batch process model parameter variation using a polynomial regression scheme in a moving horizon fashion.

Furthermore, the performance of the proposed R2R model parameter estimation scheme is evaluated with respect to different orders of polynomials and different moving horizon lengths in order to calculate the best parameter estimates.

The average crystal shape distribution of crystals produced from the closed-loop simulation of the batch crystallizer under the MPC with the proposed R2R model parameter estimation scheme is much closer to a desired set-point value compared to those of the double exponentially weighted moving average-based MPC (dEWMA-based MPC) and that of MPC based on the nominal process model.

In the "Data" section, we detail the input data, while the model parameterization and estimation scheme are described in  the "Field modelling" section.

The CHAOS-5 model parameterization and estimation scheme has been reported, and details given concerning how the candidate field models for IGRF-12 were extracted.

The estimation scheme requires a model of the underlying process of genetic change, a detection scheme that filters this process into observable quantities, and a monitoring scheme that describes the timing of observations.

Hemodynamic and coupling parameters are estimated using a Bayesian estimation scheme, such that the BOLD signals obtained with the joint forward model are as similar as possible to the observed BOLD responses (Friston et al. 2003; Mechelli et al. 2003).

In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.