A comparison between two interval arithmetic learning algorithms

Two generalizations of multilayer feedforward and backpropagation to interval arithmetic were proposed several years ago. These generalizations have several applications like the codification of rules in the training set, “don´t care attributes”, missing inputs, etc. There are two generalizations what means that there are two different training algorithms and there is no way, at first, to consider one of them better than the other. We present an in depth comparison between the two training algorithms. We have used a total number of 75 different problems and five different performance definitions for the comparison. The results are that the performance of the algorithms depends on the performance definition, but we can consider one of them as the “usual” one and in this sense one algorithm outperforms the other