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An estimator-based backstepping controller is presented with an estimator designed to provide a precise estimation of the disturbance and uncertainties.
We propose a jackknife minimum distance estimator designed to reduce the finite-sample bias of the optimal minimum distance estimator.
We can explore this hypothesis by summing the HRC with a motion estimator designed to respond specifically to 3-point correlations.
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This requires adapted frequency-domain estimators designed to handle large amount of data in a reasonable amount of time.
This requires adapted estimators designed to handle large amount of data in a reasonable amount of time.
Standard errors were calculated by estimators designed to be used with simple random sampling.
An infinite dimensional estimator is designed to estimate the disturbance.
In the second step, a current estimator is designed to estimate the damper winding currents, from which the parameters of field and damper windings are estimated.
To provide robust estimations, the estimator is designed to evaluate the upper and lower bounds on the outputs under model parameter uncertainties.
For each subsystem, a local estimator is designed to estimate the interactions and states of the subsystem, using only the local output measurements.
Under these circumstances, the state estimator is designed to estimate the true concentration of the mRNA and the protein of the uncertain GRNs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com