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With the feedback of the modeling error, we give a novel nonlinear observer-controller design, based on which both the stabilization and tracking problems are solved.
Neglecting the modeling error, we use to evaluate the channel estimation variance.
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For NO2, we evaluated modeling error against the central-site and home outdoor measurements, respectively.
In the non-curated branch of the BioModels Database, we identified the models with modeling errors.
For the model errors, we used the 50% values from the CDFs to not clutter the graphs too much.
To quantify the model errors, we calculated the CDF of the retransmission, reachability, and delay errors and presented them in Figure 9.
To model sensor error, we found that in-plane skin patch linear acceleration in the anterior-posterior direction could be modeled by an underdamped viscoelastic system.
To obtain the model residual error, we first subtracted the ground reference AGB from the predicted AGB.
For some common model error classes we provide rules for when the feedforward controller is effective, and we also design μ-optimal feedforward controllers.
Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results.
For a first order linear model with autocorrelated error, we have developed a set of efficient RFORDs with efficiency around ninety percent and the developed designs are very close to D-ORFODs.
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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