preparets

Prepare input and target time series data for network simulation or training

Syntax

[Xs,Xi,Ai,Ts,EWs,shift] = preparets(net,Xnf,Tnf,Tf,EW)

Description

This function simplifies the normally complex and error prone task of reformatting input and target time series. It automatically shifts input and target time series as many steps as are needed to fill the initial input and layer delay states. If the network has open-loop feedback, then it copies feedback targets into the inputs as needed to define the open-loop inputs.

Each time a new network is designed, with different numbers of delays or feedback settings, preparets can reformat input and target data accordingly. Also, each time a network is transformed with openloop, closeloop, removedelay or adddelay, this function can reformat the data accordingly.

[Xs,Xi,Ai,Ts,EWs,shift] = preparets(net,Xnf,Tnf,Tf,EW) takes these arguments,

net

Neural network

Xnf

Non-feedback inputs

Tnf

Non-feedback targets

Tf

Feedback targets

EW

Error weights (default = {1})

and returns,

Xs

Shifted inputs

Xi

Initial input delay states

Ai

Initial layer delay states

Ts

Shifted targets

EWs

Shifted error weights

shift

The number of timesteps truncated from the front of X and T in order to properly fill Xi and Ai.

Examples

Prepare Data for Open- and Closed-Loop Networks

Here a time-delay network with 20 hidden neurons is created, trained and simulated.

[X,T] = simpleseries_dataset;
net = timedelaynet(1:2,20);
[Xs,Xi,Ai,Ts] = preparets(net,X,T);
net = train(net,Xs,Ts);
view(net)
Y = net(Xs,Xi,Ai);

Here a NARX network is designed. The NARX network has a standard input and an open-loop feedback output to an associated feedback input.

[X,T] = simplenarx_dataset;
net = narxnet(1:2,1:2,20);
[Xs,Xi,Ai,Ts] = preparets(net,X,{},T);
net = train(net,Xs,Ts,Xi,Ai);
view(net)
y = net(Xs,Xi,Ai);

Now the network is converted to closed loop, and the data is reformatted to simulate the network’s closed-loop response.

net = closeloop(net);
view(net)
[Xs,Xi,Ai] = preparets(net,X,{},T);
y = net(Xs,Xi,Ai);

See Also

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Introduced in R2010b

Deep Learning Toolbox Documentation

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