http://www.cnr.it/ontology/cnr/individuo/prodotto/ID30802

Energy-based predictions in Lorenz system by a unified formalism and neural network modelling (Articolo in rivista)

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Energy-based predictions in Lorenz system by a unified formalism and neural network modelling (Articolo in rivista) (literal)

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Pasini, A. (1); Langone, R (2);, Maimone, F. (3); Pelino, V. (3) (2010)
Energy-based predictions in Lorenz system by a unified formalism and neural network modelling
in Nonlinear processes in geophysics; Copernicus Gesellschaft MBH, Gottingen (Germania)
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1) CNR, Institute of Atmospheric Pollution Research, Rome, Italy; 2) Katholieke Universiteit Leuven, Department ESAT/SISTA, Leuven, Belgium; 3) Italian Air Force, CNMCA, Pratica di Mare (Rome), Italy; (literal)

Titolo

Energy-based predictions in Lorenz system by a unified formalism and neural network modelling (literal)

Abstract

In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results. (literal)

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