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Zhang et al. [10] consider a new hot-rolled strip thickness model prediction method based on extreme learning machine (ELM).
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1. Polynomial model based prediction method The polynomial model for rotor angle prediction can be expressed as follows: overset{frown }{delta }(t)={a}_0+{a}_1t+{a}_n{t}^n+cdots +{a}_n{t}^n (1) where ( overset{frown }{delta }(t) ) is the predicted rotor angle at moment t.
Auto regression model based prediction method The auto regression model of the generator angle can be written as.
Trigonometric function model based prediction method The trigonometric function model for the perturbed rotor angle trajectory prediction can be described as.
Polynomial model based prediction method.
Trigonometric function model based prediction method.
Auto regression model based prediction method.
As same as the polynomial model based prediction method, if a new measurement is obtained, the measurement vector is updated and then the latest predicted value can be figured out by rolling prediction.
Linear mixed effects models (LMMs) offer a modeling and prediction method that is very effective on clustered or spatially correlated data [21, 22].
Linear mixed effects models (LMMs) offer a modeling and prediction method that is very effective on clustered or spatially correlated data [ 21, 22].
We performed our simulations to get the optimal flux distribution that is the model prediction (see methods).
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