Artificial Neural Networks have achieved satisfactory
results in different fields such as example classification or
image identification. Real-world processes usually have a
temporal evolution, and they are the type of processes
where Recurrent Networks have special success.
Nevertheless they are still reluctantly used, mainly due to
the fact that they do not adequately justify their response.
But, if ANNs offer good results, why giving them up?
Suffice it to find a method that might search an
explanation to the outputs that the ANN provides.
This work presents a technique, totally independent from
ANN architecture and the learning algorithm used, which
makes possible the justification of the ANN outputs by
means of expression trees.