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 |