postmnmx (Neural Network Toolbox)

Neural Network Toolbox

postmnmx

Postprocess data that has been preprocessed by premnmx

Syntax

[P,T] = postmnmx(PN,minp,maxp,TN,mint,maxt)

[p] = postmnmx(PN,minp,maxp)

Description

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

postmnmx takes these inputs,

PN -- R x Q matrix of normalized input vectors

minp -- R x 1 vector containing minimums for each P

maxp -- R x 1 vector containing maximums for each P

TN -- S x Q matrix of normalized target vectors

mint -- S x 1 vector containing minimums for each T

maxt -- S x 1 vector containing maximums for each T

and returns,

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

T -- R x Q matrix of target vectors

Examples

In this example we normalize a set of training data with premnmx, create and train a network using the normalized data, simulate the network, unnormalize the output of the network using postmnmx, 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,minp,maxp,tn,mint,maxt] = premnmx(p,t);
net = newff(minmax(pn),[5 1],{'tansig' 'purelin'},'trainlm');
net = train(net,pn,tn);
an = sim(net,pn);
[a] = postmnmx(an,mint,maxt);
[m,b,r] = postreg(a,t);

Algorithm

```
p = 0.5(pn+1)*(maxp-minp) + minp;
```

See Also

premnmx, prepca, poststd

poslin postreg