Neural Network Toolbox

Preprocessing and Postprocessing

Preprocessing the network inputs and targets improves the efficiency of neural network training. Postprocessing enables detailed analysis of network performance. Neural Network Toolbox provides preprocessing and postprocessing functions and Simulink blocks that enable you to:

  • Reduce the dimensions of the input vectors using principal component analysis
  • Perform regression analysis between the network response and the corresponding targets
  • Scale inputs and targets so they fall in the range [-1,1]
  • Normalize the mean and standard deviation of the training set
  • Use automated data preprocessing and data division when creating your networks
Postprocessing plots to analyze network performance.
Postprocessing plots to analyze network performance, including mean squared error validation performance for successive training epochs (top left), error histogram (top right), and confusion matrices (bottom) for training, validation, and test phases.
Next: Improved Generalization

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