 dhardlim | % |
| dividerand | % Divide the vectors in training, validation and test group according to |
| dposlin | % @code{poslin} is a positive linear transfer function used |
| dsatlin | % |
| dsatlins | % A neural feed-forward network will be trained with @code{trainlm} |
| hardlim | % |
| hardlims | % |
| ind2vec | % @code{vec2ind} convert indices to vector |
| isposint | % @code{isposint} returns true for positive integer values. |
| logsig | % @code{logsig} is a non-linear transfer function used to train |
| mapstd | % Map values to mean 0 and standard derivation to 1. |
| min_max | % @code{min_max} returns variable Pr with range of matrix rows |
| newff | % @code{newff} create a feed-forward backpropagation network |
| newp | % @code{newp} create a perceptron |
| poslin | % @code{poslin} is a positive linear transfer function used |
| poststd | % @code{poststd} postprocesses the data which has been preprocessed by @code{prestd}. |
| prestd | % @code{prestd} preprocesses the data so that the mean is 0 and the standard deviation is 1. |
| purelin | % @code{purelin} is a linear transfer function used |
| satlin | % A neural feed-forward network will be trained with @code{trainlm} |
| satlins | % A neural feed-forward network will be trained with @code{trainlm} |
| saveMLPStruct | % @code{saveStruct} saves a neural network structure to *.txt files |
| sim | % @code{sim} is usuable to simulate a before defined neural network. |
| subset | % @code{subset} splits the main data matrix which contains inputs and targets into 2 or 3 subsets |
| tansig | % @code{tansig} is a non-linear transfer function used to train |
| train | % A neural feed-forward network will be trained with @code{train} |
| trastd | % @code{trastd} preprocess additional data for neural network simulation. |
| vec2ind | % @code{vec2ind} convert vectors to indices |
Master index