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Fix cross-reference

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jfmcarreira
2018-12-15 02:10:11 +00:00
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043_DynamicErrorRobustness/DynamicErrorRobustness.tex
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@@ -264,7 +264,7 @@ Finally, the output with the highest value is selected and the corresponding str
In the proposed approach a shallow \ac{nn} using three layer, with 32 nodes in the hidden layer is used for simplicity and to avoid over-fitting, due to the limited test cases~\cite{NeuralNetworkDesign_2014}.
By avoiding over-fitting, it is guaranteed that the proposed method would continue to work in future test conditions, not used for the network training.
The number of nodes in the hidden layer was determined by comparing the performance obtained for different number of neurons in this layer using the cross-entropy performance metric~\ref{CrossEntropy_2005}.
The number of nodes in the hidden layer was determined by comparing the performance obtained for different number of neurons in this layer using the cross-entropy performance metric~\cite{CrossEntropy_2005}.
However, different network topologies with different number of neurons in each layer could also be used to predict between different error resilience techniques, as in the proposed method.
\subsubsection{Training and validation}