nnet
A feed forward multi-layer neural network.
Select category:
`newff' create a feed-forward backpropagation network
Map values to mean 0 and standard derivation to 1.
`prestd' preprocesses the data so that the mean is 0 and the standard deviation is 1.
`poststd' postprocesses the data which has been preprocessed by `prestd'.
`trastd' preprocess additional data for neural network simulation.
`sim' is usuable to simulate a before defined neural network.
A neural feed-forward network will be trained with `train'
`logsig' is a non-linear transfer function used to train neural networks.
`purelin' is a linear transfer function used by neural networks
Radial basis transfer function.
`tansig' is a non-linear transfer function used to train neural networks.
Divide the vectors in training, validation and test group according to the informed ratios
`vec2ind' convert indices to vector
`isposint' returns true for positive integer values.
`min_max' returns variable Pr with range of matrix rows
`saveStruct' saves a neural network structure to *.
`subset' splits the main data matrix which contains inputs and targets into 2 or 3 subsets depending on the parameters.
`vec2ind' convert vectors to indices
