distdelaynet

(To be removed) Distributed delay network

distdelaynet will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

distdelaynet(delays,hiddenSizes,trainFcn)


Description

Distributed delay networks are similar to feedforward networks, except that each input and layer weights has a tap delay line associated with it. This allows the network to have a finite dynamic response to time series input data. This network is also similar to the time delay neural network (timedelaynet), which only has delays on the input weight.

distdelaynet(delays,hiddenSizes,trainFcn) takes these arguments,

delays	Row vector of increasing 0 or positive delays (default = 1:2)
hiddenSizes	Row vector of one or more hidden layer sizes (default = 10)
trainFcn	Training function (default = 'trainlm')

and returns a distributed delay neural network.

Examples

Distributed Delay Network

This example shows how to train a distributed delay neural network to solve a simple time series problem.

[X,T] = simpleseries_dataset;
net = distdelaynet({1:2,1:2},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)

perf = 
0.0323

Version History

Introduced in R2010b

R2026a: distdelaynet will be removed

distdelaynet will be removed in a future release.

To train a neural network with time series data, use one of these options:

• Time Series Modeler app — Interactively design and train neural networks for time series data.

• trainnet — Train neural network. Use this function when you need more flexibility in the network architecture or training algorithm.

To learn more about time series data workflows, see these examples and topics:

• Training Neural Networks with Time Series Data

• Time Series Forecasting Using Deep Learning

• Create and Train NARX Network for Time Series Forecasting

• Long Short-Term Memory Neural Networks

• Example Deep Learning Network Architectures

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.