  It is not, in general, possible to have access to all variables that
determine the behavior of a system. Having identified a number of variables
whose values can be accessed, there may still be hidden variables which
influence the dynamics of the system. The result is model ambiguity in the
sense that, for the same (or very similar) input values, different objective
outputs should have been obtained. In addition, the degree of ambiguity may
vary widely across the whole range of input values. Thus, to evaluate the
accuracy of a model it is of utmost importance to create a method to obtain the
degree of reliability of each output result. In this paper we present such a
scheme composed of two coupled artificial neural networks: the first one being
responsible for outputting the predicted value, whereas the other evaluates the
reliability of the output, which is learned from the error values of the first
one. As an illustration, the scheme is applied to a model for tracking slopes
in a straw chamber and to a credit scoring model.
