TY - GEN
T1 - Optimized joint NARX ANN - embedded processor design methodology
AU - Possignolo, Rafael Trapani
AU - Hammami, Omar
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Neural Networks are largely used in a vast number of applications, including time series prediction, function approximation, pattern classification. Recently Nonlinear Auto Regressive with exogenous input (NARX) Recurrent Neural Networks has been used in to predict noisy and large time series (also referred as chaotic time series). This paper present a multiobjective optimized implementation of NARX neural network, specially designed to work on embedded systems.
AB - Neural Networks are largely used in a vast number of applications, including time series prediction, function approximation, pattern classification. Recently Nonlinear Auto Regressive with exogenous input (NARX) Recurrent Neural Networks has been used in to predict noisy and large time series (also referred as chaotic time series). This paper present a multiobjective optimized implementation of NARX neural network, specially designed to work on embedded systems.
U2 - 10.1109/ICECS.2009.5410883
DO - 10.1109/ICECS.2009.5410883
M3 - Conference contribution
AN - SCOPUS:77951469437
SN - 9781424450916
T3 - 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009
SP - 499
EP - 502
BT - 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009
T2 - 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009
Y2 - 13 December 2009 through 16 December 2009
ER -