  In this paper we shall review the common problems associated with Piecewise
Linear Separation incremental algorithms. This kind of neural models yield poor
performances when dealing with some classification problems, due to the
evolving schemes used to construct the resulting networks. So as to avoid this
undesirable behavior we shall propose a modification criterion. It is based
upon the definition of a function which will provide information about the
quality of the network growth process during the learning phase. This function
is evaluated periodically as the network structure evolves, and will permit, as
we shall show through exhaustive benchmarks, to considerably improve the
performance(measured in terms of network complexity and generalization
capabilities) offered by the networks generated by these incremental models.
