These constants represent the training algorithms available within the fann library. The list will grow over time, but probably not shrink.
The training algorithm used by this function is chosen by the
fann_set_training_algorithm
function. The default training algorithm is FANN_TRAIN_RPROP.
Constants
Standard backpropagation algorithm, where the weights are updated after each training pattern. This means that the weights are updated many times during a single epoch. For this reason some problems, will train very fast with this algorithm, while other more advanced problems will not train very well.
Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. This means that the weights are only updated once during a epoch. For this reason some problems, will train slower with this algorithm. But since the mean square error is calculated more correctly than in incremental training, some problems will reach a better solutions with this algorithm.
A more advanced batch training algorithm which achieves good results for many problems. The RPROP training algorithm is adaptive, and does therefore not use the learning_rate. Some other parameters can however be set to change the way the RPROP algorithm works, but it is only recommended for users with insight in how the RPROP training algorithm works.
The RPROP training algorithm is described in [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the iRPROP- training algorithm [Igel and Hüsken, 2000] which is an variety of the standard RPROP training algorithm.
A more advanced batch training algorithm which achieves good results for many problems. The quickprop training algorithm uses the learning_rate parameter along with other more advanced parameters, but it is only recommended to change these advanced parameters, for users with insight in how the quickprop training algorithm works.
The quickprop training algorithm is described in [Fahlman, 1988].