radu/Master-Thesis
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Fixed unconsistent use of acronyms

Browse source
This commit is contained in:
Radu C. Martin 2021-07-22 22:13:51 +02:00
parent 1e1cc5acd8
commit 721953642c
7 changed files with 49 additions and 47 deletions
Download patch file Download diff file Expand all files Collapse all files

10
Sections/50_Choice_of_Hyperparameters.tex
View file

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