Neural Network Toolbox

Mean and Stand. Dev. (prestd, poststd, trastd)

Another approach for scaling network inputs and targets is to normalize the mean and standard deviation of the training set. This procedure is implemented in the function prestd. It normalizes the inputs and targets so that they will have zero mean and unity standard deviation. The following code illustrates the use of prestd.

• [pn,meanp,stdp,tn,meant,stdt] = prestd(p,t);
  

The original network inputs and targets are given in the matrices p and t. The normalized inputs and targets, pn and tn, that are returned will have zero means and unity standard deviation. The vectors meanp and stdp contain the mean and standard deviations of the original inputs, and the vectors meant and stdt contain the means and standard deviations of the original targets. After the network has been trained, these vectors should be used to transform any future inputs that are applied to the network. They effectively become a part of the network, just like the network weights and biases.

If prestd is used to scale both the inputs and targets, then the output of the network is trained to produce outputs with zero mean and unity standard deviation. If you want to convert these outputs back into the same units that were used for the original targets, then you should use the routine poststd. In the following code we simulate the network that was trained in the previous code, and then convert the network output back into the original units.

• an = sim(net,pn);
  a = poststd(an,meant,stdt);
  

The network output an corresponds to the normalized targets tn. The un-normalized network output a is in the same units as the original targets t.

If prestd is used to preprocess the training set data, then whenever the trained network is used with new inputs, they should be preprocessed with the means and standard deviations that were computed for the training set. This can be accomplished with the routine trastd. In the following code, we apply a new set of inputs to the network we have already trained.

• pnewn = trastd(pnew,meanp,stdp);
  anewn = sim(net,pnewn);
  anew = poststd(anewn,meant,stdt);
  

Preprocessing and Postprocessing

Principal Component Analysis (prepca, trapca)

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