Fixed unconsistent use of acronyms
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7 changed files with 49 additions and 47 deletions
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@ -19,7 +19,7 @@ consuming computations in the case of larger number of regressors and more
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complex kernel functions.
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As described in Section~\ref{sec:gp_dynamical_system}, for the purpose of this
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project, the \acrlong{gp} model will be trained using the \acrshort{narx}
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project, the \acrshort{gp} model will be trained using the \acrshort{narx}
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structure. This already presents an important choice in the selection of
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regressors and their respective autoregressive lags.
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@ -185,7 +185,7 @@ $l_u = 1$ and $l_y = 3$ with $l_w$ taking the values of either 1, 2 or 3,
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depending on the results of further analysis.
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As for the case of the \acrlong{svgp}, the results for the classical
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As for the case of the \acrshort{svgp}, the results for the classical
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\acrshort{gp} (cf. Table~\ref{tab:GP_hyperparameters}) are not necessarily
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representative of the relationships between the regressors of the
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\acrshort{svgp} model, due to the fact that the dataset used for training is
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@ -259,8 +259,8 @@ This performance metric is very useful when training a model whose goal is
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solely to minimize the difference between the measured values, and the ones
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predicted by the model.
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A variant of the \acrshort{mse} is the \acrfull{smse}, which normalizes the
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\acrlong{mse} by the variance of the output values of the validation dataset.
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A variant of the \acrfull{mse} is the \acrfull{smse}, which normalizes the
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\acrshort{mse} by the variance of the output values of the validation dataset.
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\begin{equation}\label{eq:smse}
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\text{SMSE} = \frac{1}{N}\frac{\sum_{i=1}^N \left(y_i -
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@ -403,7 +403,7 @@ the discrepancies.
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\subsubsection{Conventional Gaussian Process}
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The simulation performance of the three lag combinations chosen for the
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classical \acrlong{gp} models has been analyzed, with the results presented in
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classical \acrshort{gp} models has been analyzed, with the results presented in
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Figures~\ref{fig:GP_113_multistep_validation},~\ref{fig:GP_213_multistep_validation}
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and~\ref{fig:GP_313_multistep_validation}. For reference, the one-step ahead
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predictions for the training and test datasets are presented in
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