This article presents the development of a neural
network cognitive model for the classification and detection of
different frequency signals. The basic structure of the implemented
neural network was inspired on the perception process that humans
generally make in order to visually distinguish between high and low
frequency signals. It is based on the dynamic neural network concept,
with delays. A special two-layer feedforward neural net structure was
successfully implemented, trained and validated, to achieve
minimum target error. Training confirmed that this neural net
structure descents and converges to a human perception classification
solution, even when far away from the target.
 Bruce Goldstein, "Sensation and Perception", Sixth Edition,
 Paulo Gil, "Redes Neuronais Artificiais na Modela├º├úo e Controlo de
Sistemas Din├ómicos", Controlo Inteligente, DEE/FCT/UNL.
 Leslie Smith, "An Introduction to Neural Networks", Department of
Computing Mathematics, Centre for Cognitive and Computational
Neuroscience, University of Stirling, UK, 2003. Available:
 Howard Demuth, Mark Beale, "Neural Network Toolbox For Use with
MATLAB - User-s Guide Version 3.0", The MathWorks, Inc, 1992.