D  
deriv_error [Ann_func] 
Type of the derivative of the error function

deriv_out [Ann_func] 
Type of the derivative of the transfer function of the output layer

G  
gradient_learning [Ann_config] 
Type allowing to choose the number of patterns used to compute the
gradient of the error, before making a step in the descent direction
(in the weights space).

I  
input [Annet]  
M  
mode [Ann_config] 
The program can be runned in several modes

N  
nn_func [Ann_func] 
Error function, tranfer functions for the hidden and output units, and
their derivatives.

nn_topology [Ann_topology] 
Description of the networks units, layers, and connections.

O  
opti [Ann_config] 
Type of optimization method used to minimize the error.

out_choice [Ann_func] 
The derivatives of the activation function of the output layer
and of the error fuction are used to compute the deltas of the
output layer (see Ann_backprop, and [Bishop96], section 4.8).

V  
vector [Ann_func] 