Neural Network Toolbox

poststd

Postprocess data which has been preprocessed by prestd

Syntax

[P,T] = poststd(PN,meanp,stdp,TN,meant,stdt)

[p] = poststd(PN,meanp,stdp)

Description

poststd postprocesses the network training set that was preprocessed by prestd. It converts the data back into unnormalized units.

poststd takes these inputs,

• PN    - R x Q matrix of normalized input vectors.

  meanp - R x 1 vector containing standard deviations for each P.

  stdp  - R x 1 vector containing standard deviations for each P.

  TN    - S x Q matrix of normalized target vectors.

  meant - S x 1 vector containing standard deviations for each T.

  stdt  - S x 1 vector containing standard deviations for each T.

and returns,

• P - R x Q matrix of input (column) vectors.

  T - S x Q matrix of target vectors.

Examples

In this example we normalize a set of training data with prestd, create and train a network using the normalized data, simulate the network, unnormalize the output of the network using poststd, and perform a linear regression between the network outputs (unnormalized) and the targets to check the quality of the network training.

• p = [-0.92 0.73 -0.47 0.74 0.29; -0.08 0.86 -0.67 -0.52 0.93];
  t = [-0.08 3.4 -0.82 0.69 3.1];
  [pn,meanp,stdp,tn,meant,stdt] = prestd(p,t);
  net = newff(minmax(pn),[5 1],{'tansig' 'purelin'},'trainlm');
  net = train(net,pn,tn);
  an = sim(net,pn);
  a = poststd(an,meant,stdt);
  [m,b,r] = postreg(a,t);
  

Algorithm

• p = stdp*pn + meanp;
  

See Also

premnmx, prepca, postmnmx, prestd

postreg

premnmx