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An autoregressive with exogenous inputs (ARX -based predictor wARX -basedd to construct a CL-thalamic DBS model and forecast the future predictorical marker according to the past physiological marker and applied DBS.
One possible explanation for this dichotomy is that the big-PI predictor was designed based on a limited number of known fungal GPI-T signal sequences.
A number of different disorder prediction software and web services have been developed since the first predictor was designed by Dunker's lab in 1997.
Initially, this predictor was designed to be a general prognostic tool, developed in a mixed population of patients receiving chemotherapy and tamoxifen.
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In order to address this issue, a predictor is designed.
To handle the delays and data losses simultaneously, a predictor is designed for each subsystem estimator.
The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality.
Then, an adaptive predictor is designed based on the observer and is employed to predict non-measurable states.
With the non-persistent measurement data, a linear predictor is designed to provide the reference signal in the adaptive filter.
Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed.
With the obtained nominal model, uncertainty bound and the robust criterion derived, a robust Smith predictor is designed.
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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