title: Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor.
abstract: Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and a local nonlinear predictor. We compare the performances of these three methods.
authors: Department of Chemistry, Biology and Biotechnology, University of Perugia, 06123 Perugia, Italy.
authors: Pier Luigi
authors: PL
authors: Gentili
authors: Department of Mechanical Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi, Shiga 525-8577, Japan.
authors: Hiroshi
authors: H
authors: Gotoda
authors: Department of Chemistry, Brandeis University, Waltham, Massachusetts 02454-9110, USA.
authors: Milos
authors: M
authors: Dolnik
authors: Department of Chemistry, Brandeis University, Waltham, Massachusetts 02454-9110, USA.
authors: Irving R
authors: IR
authors: Epstein