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This dissertation investigates the development of
Artificial Neural Network (ANN) in the solution of
problems where the patterns presented to the network have
a temporary relationship to each other, such as time
series forecast and voice processing.
Temporary ANN considers the time in its operation,
incorporating memory of short period distributed in the
network in all the hidden neurons and in the output
neurons in some cases. This class of network in better
used to represent the temporary nature of the dynamic
systems. In contrast, Static ANN has a structure adapted
for tasks of pattern recognition, classification and
another static or stationary problems, achieving great
success in several applications. Considered an universal
approximator, Static ANN has also been used in
applications of dynamic systems, through some artifices in
the input of the network and through statistical data pre-
The objective of this work is, therefore to study
the theory and evaluate the performance of Temporal ANN,
in comparison with Static ANN, in applications of dynamics
systems. The development of this research involved 3 main
stages: bibliographical research of the methodologies
developed for Temporal ANN; selection and implementation
of the models for the evaluation of these networks; and
The bibliographical research allowed to compile
and to classify the main on Temporal ANN, Typically, these
network was selected, where the synapses are filters FIR
(Finite-duration Impulse Response) that represent the
temporary nature of the problem. The FIR network has been
selected since it includes practically all other methods
of its class, presenting a more formal mathematical model.
On the second group, the Elman recurrent network was
considered, that presents global feedback of each neuron
in the hidden layer to all other neurons in this layer.
In the case studies the network selected have been
tested in two application: forecast of time series and
digital signal processing. In the case of forecast, result
of electric energy consumption time series prediction were
compared with the result found in the literature such as
Holt-Winters, Box & Jenkins and Static ANN methods. In the
case of the application of processing where the
comparisons were made with the results presented by the
standard neural filter, made of a multilayer feed-forward
network with the back propagation learning algorithm.
This work showed in practice that Temporal ANN
captures the characteristics of the temporary processes in
a more efficient way that Static ANN and other methods,
being able to learn the non stationary behavior of the
temporary series directly. The results showed that the FIR
neural network and de Elman network learned better the
complexity of the voice signals.