Title :
Optimized joint NARX ANN - embedded processor design methodology
Author :
Possignolo, Rafael Trapani ; Hammami, Omar
Author_Institution :
ENSTA - ParisTech, Paris, France
Abstract :
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.
Keywords :
function approximation; logic design; optimisation; pattern classification; recurrent neural nets; time series; chaotic time series; embedded processor design; function approximation; noise prediction; nonlinear auto regressive with exogenous input; optimized joint NARX ANN; pattern classification; recurrent neural networks; time series prediction; Computer architecture; Design methodology; Design optimization; Embedded software; Entropy; Hardware; Neural networks; Neurons; Predictive models; Recurrent neural networks;
Conference_Titel :
Electronics, Circuits, and Systems, 2009. ICECS 2009. 16th IEEE International Conference on
Conference_Location :
Yasmine Hammamet
Print_ISBN :
978-1-4244-5090-9
Electronic_ISBN :
978-1-4244-5091-6
DOI :
10.1109/ICECS.2009.5410883
