In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.
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Paolo ARENA, Riccardo CAPONETTO, Luigi FORTUNA, Giovanni MUSCATO, Maria Gabriella XIBILIA, "Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 10, pp. 1682-1688, October 1996, doi: .
Abstract: In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_10_1682/_p
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@ARTICLE{e79-a_10_1682,
author={Paolo ARENA, Riccardo CAPONETTO, Luigi FORTUNA, Giovanni MUSCATO, Maria Gabriella XIBILIA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction},
year={1996},
volume={E79-A},
number={10},
pages={1682-1688},
abstract={In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1682
EP - 1688
AU - Paolo ARENA
AU - Riccardo CAPONETTO
AU - Luigi FORTUNA
AU - Giovanni MUSCATO
AU - Maria Gabriella XIBILIA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1996
AB - In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.
ER -