narxnet

Nonlinear autoregressive neural network with external input

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Syntax

narxnet(inputDelays,feedbackDelays,hiddenSizes,feedbackMode,trainFcn)

Description

NARX (Nonlinear autoregressive with external input) networks can learn to predict one time series given past values of the same time series, the feedback input, and another time series, called the external or exogenous time series.

narxnet(inputDelays,feedbackDelays,hiddenSizes,feedbackMode,trainFcn) takes these arguments,

inputDelays

Row vector of increasing 0 or positive delays (default = 1:2)

feedbackDelays

Row vector of increasing 0 or positive delays (default = 1:2)

hiddenSizes

Row vector of one or more hidden layer sizes (default = 10)

feedbackModeOne of 'open', 'closed', or 'none' (default is 'open')
trainFcn

Training function (default is 'trainlm')

and returns a NARX neural network.

Examples

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Open Script

Partition the training data. Use Xnew to do prediction in closed loop mode later.

[X,T] = simpleseries_dataset;
Xnew = X(81:100);
X = X(1:80);
T = T(1:80);

Train a network, and simulate it on the first 80 observations

net = narxnet(1:2,1:2,10);
[Xs,Xi,Ai,Ts] = preparets(net,X,{},T);
net = train(net,Xs,Ts,Xi,Ai);
view(net)

Calculate the network performance.

[Y,Xf,Af] = net(Xs,Xi,Ai);
perf = perform(net,Ts,Y)

perf =

    0.0153

Run the prediction for 20 time steps ahead in closed loop mode.

[netc,Xic,Aic] = closeloop(net,Xf,Af);
view(netc)


y2 = netc(Xnew,Xic,Aic)

y2 =

  1x20 cell array

  Columns 1 through 5

    {[-0.0156]}    {[0.1133]}    {[-0.1472]}    {[-0.0706]}    {[0.0355]}

  Columns 6 through 10

    {[-0.2829]}    {[0.2047]}    {[-0.3809]}    {[-0.2836]}    {[0.1886]}

  Columns 11 through 15

    {[-0.1813]}    {[0.1373]}    {[0.2189]}    {[0.3122]}    {[0.2346]}

  Columns 16 through 20

    {[-0.0156]}    {[0.0724]}    {[0.3395]}    {[0.1940]}    {[0.0757]}

See Also

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Introduced in R2010b

Deep Learning Toolbox Documentation
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