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The autoassociative memory performs clustering by finding a prototype to the given input.
The implementation assumes two-dimensional input and output (cf. art1).
assoz(x, ...)# S3 method for default
assoz(x, dimX, dimY, maxit = 100,
initFunc = "RM_Random_Weights", initFuncParams = c(1, -1),
learnFunc = "RM_delta", learnFuncParams = c(0.01, 100, 0, 0, 0),
updateFunc = "Auto_Synchronous", updateFuncParams = c(50),
shufflePatterns = TRUE, ...)
a matrix with training inputs for the network
additional function parameters (currently not used)
x dimension of inputs and outputs
y dimension of inputs and outputs
maximum of iterations to learn
the initialization function to use
the parameters for the initialization function
the learning function to use
the parameters for the learning function
the update function to use
the parameters for the update function
should the patterns be shuffled?
an rsnns object. The fitted.values member contains the
activation patterns for all inputs.
The default initialization and update functions are the only ones suitable for this kind of
network. The update function takes one parameter, which is the number of iterations that will
be performed. The default of 50 usually does not have to be modified. For learning, RM_delta
and Hebbian functions can be used, though the first one usually performs better.
A more detailed description of the theory and the parameters is available from the SNNS documentation and the other referenced literature.
Palm, G. (1980), 'On associative memory', Biological Cybernetics 36, 19-31.
Rojas, R. (1996), Neural networks :a systematic introduction, Springer-Verlag, Berlin.
Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of T<U+00FC>bingen. http://www.ra.cs.uni-tuebingen.de/SNNS/
# NOT RUN {
demo(assoz_letters)
# }
# NOT RUN {
demo(assoz_lettersSnnsR)
# }
# NOT RUN {
data(snnsData)
patterns <- snnsData$art1_letters.pat
model <- assoz(patterns, dimX=7, dimY=5)
actMaps <- matrixToActMapList(model$fitted.values, nrow=7)
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(actMaps[[i]])
# }
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