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| poststd | Examples See Also |
Postprocess data which has been preprocessed by prestd
[p,t] = poststd(pn,meanp,stdp,tn,meant,stdt)
[p] = poststd(pn,meanp,stdp)
poststd postprocesses the network training set which 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.
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);
p = stdp*pn + meanp;
premnmx, prepca, postmnmx, prestd