rsnnsObjectFactory: Object factory for generating rsnns objects

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

Object factory for generating rsnns objects

Description

The object factory generates an rsnns object and initializes its member variables with the values given as parameters. Furthermore, it generates an object of SnnsR-class. Later, this information is to be used to train the network.

Usage

rsnnsObjectFactory(
  subclass,
  nInputs,
  maxit,
  initFunc,
  initFuncParams,
  learnFunc,
  learnFuncParams,
  updateFunc,
  updateFuncParams,
  shufflePatterns = TRUE,
  computeIterativeError = TRUE,
  pruneFunc = NULL,
  pruneFuncParams = NULL
)

Arguments

subclass	the subclass of rsnns to generate (vector of strings)
nInputs	the number of inputs the network will have
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?
computeIterativeError	should the error be computed in every iteration?
pruneFunc	the pruning function to use
pruneFuncParams	the parameters for the pruning function. Unlike the other functions, these have to be given in a named list. See the pruning demos for further explanation.

Details

The typical procedure implemented in rsnns subclasses is the following:

• generate the rsnns object with this object factory

• generate the network according to the architecture needed

• train the network (with train)

In every rsnns object, the iterative error is the summed squared error (SSE) of all patterns. If the SSE is computed on the test set, then it is weighted to take care of the different amount of patterns in the sets.

Value

a partly initialized rsnns object

See Also

mlp, dlvq, rbf, rbfDDA, elman, jordan, som, art1, art2, artmap, assoz

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