neto = layrecnet(inputDelays,hiddenLayerSize);
[Xo,Xoi,Aoi,To] = preparets(neto,Xorig,Torig);
[ neto, tro, Yo, Eo, Xof, Aof ] = train(neto,Xo,To,Xoi,Aoi);
view(neto)
Yo = neto(Xo,Xoi,Aoi);
to = cell2mat(To);
MSE00o = mean(var(to',1))
NMSEo = mse(Eo)/MSE00o
R2o = 1 - NMSEo
yo = cell2mat(Yo);
nets = removedelay(neto);
Layer recurrent networks with two (or more) layers can learn to
predict any dynamic output from past inputs given enough hidden
neurons and enough recurrent layer delays.
layrecnet(layerDelays,hiddenSizes,trainFcn) takes a row vectors
of layers delays, a row vector of hidden layer sizes, and a
backpropagation training function, and returns a layer recurrent neural
network with N+1 layers.
Input, output and output layers sizes are set to 0. These sizes will
automatically be configured to match particular data by train. Or the
user can manually configure inputs and outputs with configure.
Defaults are used if layrecnet is called with fewer arguments.
The default arguments are (1:2,10,'trainlm').
Here a layer recurrent network is used to solve a time series problem.
[X,T] = simpleseries_dataset;
net = layrecnet(1:2,10);
[Xs,Xi,Ai,Ts] = preparets(net,X,T);
net = train(net,Xs,Ts,Xi,Ai);
view(net);
Y = net(Xs,Xi,Ai);
perf = perform(net,Y,Ts)
To predict the next output a step ahead of when it will occur:
net = removedelay(net);
[Xs,Xi,Ai,Ts] = preparets(net,X,T);
Y = net(Xs,Xi,Ai);
See also narxnet, timedelaynet, distdelaynet.
Reference page in Help browser
doc layrecnet
