Title :
Synchronization in chaotic systems with artificial neural networks
Author :
Otawara, K. ; Fan, L.T.
Author_Institution :
Dept. of Chem. Eng., Kansas State Univ., Manhattan, KS, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Chaos is the apparently irregular motion that is, in reality, nonlinear but deterministic. Chaos exhibits extremely sensitive dependence on initial conditions; this tends to prevent the prediction the behavior of a chaotic system. It has been demonstrated, however, that chaotic systems can be synchronized by linking them with common driving signals. This study proposes a methodology for synchronizing chaos by resorting to artificial neural networks (ANN´s). The ANN´s are capable of approximately learning the chaotic behavior, and, thus, they render possible the synchronization of systems whose deterministic governing equations are not sufficiently well known. This requires training them with experimentally obtained time-series data and the aid of common driving signals. Examples are given for two iterated maps
Keywords :
chaos; learning (artificial intelligence); neural nets; synchronisation; time series; artificial neural networks; chaotic behavior; chaotic systems; deterministic governing equations; initial conditions; irregular motion; learning; synchronization; time-series data; Artificial neural networks; Chaos; Chemical engineering; Chemical reactors; Electronic mail; Feedback control; Intelligent networks; Joining processes; Nonlinear equations; Oscillators;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374735