Create and train an art2 network
ART2 is very similar to ART1, but for real-valued input. See art1
for more information. Opposed to the ART1 implementation, the ART2 implementation
does not assume two-dimensional input.
art2(x, ...) ## Default S3 method: art2(x, f2Units = 5, maxit = 100, initFunc = "ART2_Weights", initFuncParams = c(0.9, 2), learnFunc = "ART2", learnFuncParams = c(0.98, 10, 10, 0.1, 0), updateFunc = "ART2_Stable", updateFuncParams = c(0.98, 10, 10, 0.1, 0), shufflePatterns = TRUE, ...)
x |
a matrix with training inputs for the network |
... |
additional function parameters (currently not used) |
f2Units |
controls the number of clusters assumed to be present |
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? |
As comparison of real-valued vectors is more difficult than comparison of binary vectors, the comparison layer is more complex in ART2, and actually consists of three layers. With a more complex comparison layer, also other parts of the network enhance their complexity. In SNNS, this enhanced complexity is reflected by the presence of more parameters in initialization-, learning-, and update function.
In analogy to the implementation of ART1, there are one initialization function, one learning function and two update functions suitable for ART2. The learning and update functions have five parameters, the initialization function has two parameters. For details see the SNNS User Manual, p. 67 and pp. 192.
an rsnns object. The fitted.values member contains the
activation patterns for all inputs.
Carpenter, G. A. & Grossberg, S. (1987), 'ART 2: self-organization of stable category recognition codes for analog input patterns', Appl. Opt. 26(23), 4919–4930.
Grossberg, S. (1988), Adaptive pattern classification and universal recoding. I.: parallel development and coding of neural feature detectors, MIT Press, Cambridge, MA, USA, chapter I, pp. 243–258.
Herrmann, K.-U. (1992), 'ART – Adaptive Resonance Theory – Architekturen, Implementierung und Anwendung', Master's thesis, IPVR, University of Stuttgart. (in German)
Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html
Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)
## Not run: demo(art2_tetra)
## Not run: demo(art2_tetraSnnsR)
data(snnsData)
patterns <- snnsData$art2_tetra_med.pat
model <- art2(patterns, f2Units=5, learnFuncParams=c(0.99, 20, 20, 0.1, 0),
updateFuncParams=c(0.99, 20, 20, 0.1, 0))
model
testPatterns <- snnsData$art2_tetra_high.pat
predictions <- predict(model, testPatterns)
## Not run: library(scatterplot3d)
## Not run: par(mfrow=c(2,2))
## Not run: scatterplot3d(patterns, pch=encodeClassLabels(model$fitted.values))
## Not run: scatterplot3d(testPatterns, pch=encodeClassLabels(predictions))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.