poststd (Neural Network Toolbox)

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