NeurEco performs the data normalization automatically for Parametric Frequency Sweep.

• for input features: a Min-Max normalization is performed by feature, meaning that each input feature \(f\) is normalized independently from others, so that

  \[f_{normalized}=\frac{f-min(f)}{max(f)}\]

• for output features: all features are normalized together by division by their maximum absolute value, so that

  \[targets_{normalized}=\frac{targets}{max(|targets|)}\]