Create and train an RBF network with the DDA algorithm
Create and train an RBF network with the dynamic decay adjustment (DDA) algorithm. This type of network can only be used for classification. The training typically begins with an empty network, i.e., a network only consisting of input and output units, and adds new units successively. It is a lot easier to use than normal RBF, because it only requires two quite uncritical parameters.
rbfDDA(x, ...) ## Default S3 method: rbfDDA(x, y, maxit = 1, initFunc = "Randomize_Weights", initFuncParams = c(-0.3, 0.3), learnFunc = "RBF-DDA", learnFuncParams = c(0.4, 0.2, 5), updateFunc = "Topological_Order", updateFuncParams = c(0), shufflePatterns = TRUE, linOut = FALSE, ...)
x |
a matrix with training inputs for the network |
... |
additional function parameters (currently not used) |
y |
the corresponding targets values |
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 |
The default functions do not have to be altered. The learning function RBF-DDA has
three parameters: a positive threshold, and a negative threshold, that controls adding units to
the network, and a parameter for display purposes in the original SNNS. This parameter has
no effect in RSNNS. See p 74 of the original SNNS User Manual for details.
an rsnns object.
Berthold, M. R. & Diamond, J. (1995), Boosting the Performance of RBF Networks with Dynamic Decay Adjustment, in 'Advances in Neural Information Processing Systems', MIT Press, , pp. 521–528.
Hudak, M. (1993), 'RCE classifiers: theory and practice', Cybernetics and Systems 23(5), 483–515.
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
## Not run: demo(iris) ## Not run: demo(rbfDDA_spiralsSnnsR) data(iris) iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)] irisValues <- iris[,1:4] irisTargets <- decodeClassLabels(iris[,5]) iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15) iris <- normTrainingAndTestSet(iris) model <- rbfDDA(iris$inputsTrain, iris$targetsTrain) summary(model) plotIterativeError(model)
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