A mutually recursive method to detect and remove noise in chaotic dynamics
Year: 1994
Authors: Perrone A.L., Boccaletti S., Basti G., Arecchi F.T.
Autors Affiliation: Dept. of Physics, II Univ. of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, 00133 Rome (Italy);
Istituto Nazionale di Fisica Nucleare-INFN Section of Rome “Tor Vergata”;
Pontifical Gregorian University, P.zza della Pilotta 4, 00184 Rome (Italy);
Dept. of Physics, Univ. of Florence and Istituto Nazionale di Ottica,
Largo E. Fermi 6, 50125 Arcetri Firenze, Italy
Abstract: Recently a lot of works were published about the characterization of time-dependent processes through techniques using wavelet approach. Our work takes into account a particular class of time dependent processes in non-linear realm. We want to characterize chaotic dynamics from the standpoint of its unstable periodicities. For this aim we introduce a new technique able to stabilize such unstable orbits. We illustrate this technique both from the theoretical and the experimental standpoint. As a further step, we want to deal with the problem of detecting and removing noise from chaotic dynamics. In this paper, firstly, we show how our technique is able to distinguish with very high sensitivity between a
purely chaotic dynamics and a chaotic dynamics with noise even though the noise percentage is very low of the order of 1% only!). Secondly, we apply our technique to remove noise from this dynamics. Finally, we compare both from the theoretical and experimental standpoint our technique with the well known wavelet technique. This work is a part of “Skynnet” international project supported by the Italian INFN (National Institute for Nuclear Physics) and partially devoted to the application of new chaotic techniques instantiated in neural architectures for compressing, storing and transmitting information to earth from satellites.
Journal/Review: PROCEEDINGS OF SPIE
Volume: 2242 Pages from: 130 to: 139
KeyWords: CHAOTIC DYNAMICS; NOISE; Citations: 2data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-17References taken from IsiWeb of Knowledge: (subscribers only)