Comparison of statistical methods and fuzzy systems for atmospheric pressure wave prediction

Francesco Masulli; Franco Casalino; Renato Caviglia; Lorenzo Papa

doi:10.1117/12.205101

6 April 1995 Comparison of statistical methods and fuzzy systems for atmospheric pressure wave prediction

Francesco Masulli, Franco Casalino, Renato Caviglia, Lorenzo Papa

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205101
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Francesco Masulli, Franco Casalino, Renato Caviglia, and Lorenzo Papa "Comparison of statistical methods and fuzzy systems for atmospheric pressure wave prediction", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205101

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