elman: Create and train an Elman network

In RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

Create and train an Elman network

Description

Elman networks are partially recurrent networks and similar to Jordan networks (function jordan). For details, see explanations there.

Usage

elman(x, ...)

## Default S3 method:
elman(
  x,
  y,
  size = c(5),
  maxit = 100,
  initFunc = "JE_Weights",
  initFuncParams = c(1, -1, 0.3, 1, 0.5),
  learnFunc = "JE_BP",
  learnFuncParams = c(0.2),
  updateFunc = "JE_Order",
  updateFuncParams = c(0),
  shufflePatterns = FALSE,
  linOut = TRUE,
  outContext = FALSE,
  inputsTest = NULL,
  targetsTest = NULL,
  ...
)

Arguments

x	a matrix with training inputs for the network
...	additional function parameters (currently not used)
y	the corresponding targets values
size	number of units in the hidden layer(s)
maxit	maximum of iterations to learn
initFunc	the initialization function to use
initFuncParams	the parameters for the initialization function
learnFunc	the learning function to use
learnFuncParams	the parameters for the learning function
updateFunc	the update function to use
updateFuncParams	the parameters for the update function
shufflePatterns	should the patterns be shuffled?
linOut	sets the activation function of the output units to linear or logistic
outContext	if TRUE, the context units are also output units (untested)
inputsTest	a matrix with inputs to test the network
targetsTest	the corresponding targets for the test input

Details

Learning in Elman networks: Same as in Jordan networks (see jordan).

Network architecture: The difference between Elman and Jordan networks is that in an Elman network the context units get input not from the output units, but from the hidden units. Furthermore, there is no direct feedback in the context units. In an Elman net, the number of context units and hidden units has to be the same. The main advantage of Elman nets is that the number of context units is not directly determined by the output dimension (as in Jordan nets), but by the number of hidden units, which is more flexible, as it is easy to add/remove hidden units, but not output units.

A detailed description of the theory and the parameters is available, as always, from the SNNS documentation and the other referenced literature.

Value

an rsnns object.

References

Elman, J. L. (1990), 'Finding structure in time', Cognitive Science 14(2), 179–211.

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. https://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)

See Also

jordan

Examples

## Not run: demo(iris)
## Not run: demo(laser)
## Not run: demo(eight_elman)
## Not run: demo(eight_elmanSnnsR)


data(snnsData)
inputs <- snnsData$eight_016.pat[,inputColumns(snnsData$eight_016.pat)]
outputs <- snnsData$eight_016.pat[,outputColumns(snnsData$eight_016.pat)]

par(mfrow=c(1,2))

modelElman <- elman(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
modelElman
modelJordan <- jordan(inputs, outputs, size=8, learnFuncParams=c(0.1), maxit=1000)
modelJordan

plotIterativeError(modelElman)
plotIterativeError(modelJordan)

summary(modelElman)
summary(modelJordan)

RSNNS documentation built on May 29, 2024, 4:37 a.m.