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Proceedings Paper

Comparison of statistical methods and fuzzy systems for atmospheric pressure wave prediction
Author(s): Francesco Masulli; Franco Casalino; Renato Caviglia; Lorenzo Papa
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Paper Abstract

The prediction of complex phenomena, like the atmospheric system, represents a challenging application field for soft-computing methods. Neural networks and fuzzy systems allow one to obtain nonlinear, model-free regression methods that involve a large number of parameters and that are able to exploit much a-priori numerical knowledge (or training sets). The experiments described in this article point out that time series made up of daily average measures of atmospheric pressure and its waves are characterized by a positive first Lyapunov exponent, hence they are chaotic signals. Moreover, we compare the forecasting performances of two statistical methods, namely, the autoregressive moving average (ARMA) method and the linear predictor code (LPC) and of the adaptive fuzzy system (AFS). The AFS shows the higher prediction accuracy in each experiment, as compared with ARMA and LPC. In addition, for the AFS single waves are easier to predict than the global phenomenon, and the more accurate predictions are obtained for longer waves.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205101
Show Author Affiliations
Francesco Masulli, Univ. di Genova (Italy)
Franco Casalino, INFM-National Institute for Matter Physics (Italy)
Renato Caviglia, DISI (Italy)
Lorenzo Papa, Istituto Idrografico della Marina (Italy)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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