  In systems identification, the studied phenomena are accompanied by
uncertainties, whether arising from measurement data or computational
calculations. Interval data provides a valuable way to represent available
information on complex problems where uncertainty, inaccuracy, or variability
must be taken into account. The present work aims to determine interval
parameters for a model considering data measurement uncertainties using the MQ
estimator and neural networks. The main objective of this work is to apply this
technique in audiometric systems, particularly in the automatic detection of
auditory responses taking into consideration the flexible computing concepts
that offer solutions tolerant to subjectivity, inaccuracy or uncertainty. It
was possible to obtain a model with interval parameters that allow an infinite
set of parameters to be evaluated as a limited range. The model was validated
using two methods, one with prediction of 2 steps ahead, using the MQ estimator
and extended to another representation as neural networks 5 steps delay. It has
been shown that interval parameters generate interval results that contain most
validation data improving system reliability.
