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The performance of any approximation scheme is known to be largely dependent on the type of surrogate models and its corresponding input variables.
You might think that this type of surrogate could cause no trouble after the birth.
Variation in these results might reflect differences among studies in several key characteristics, such as, study area location, spatial extent, spatial resolution, type of surrogate, taxa (or other test features) used to evaluate surrogates, and analytical methods used to test surrogates.
There was no evidence that this relationship differed by type of surrogate.
Fourteen studies included at least one process outcome, and all of these also had some type of surrogate outcome measurement.
It seems that different Cry proteins have found the appropriate insect species, or even insect order, to interact with a same type of surrogate receptor.
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While our numerical study suggests building state-variable surrogates and using the relative error indicator for building log-likelihood surrogates, selecting appropriate type of surrogates and error indicators depends on the shapes of response surfaces.
The choice of the type of surrogates depends on the way the surrogate is resampled in the particular method.
Two types of surrogate models are used to approximate equipment phenomenological models: polynomial and neural network-based.
The accuracy, efficiency and robustness of the proposed adaptive surrogate model have also been compared between the different types of surrogate models.
In engineering, kriging is widely used because it is fast to train and is generally more accurate than other types of surrogate models.
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