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Predicting phase and sensing phase coherence in chaotic systems with machine learning
- Source :
- Chaos (Woodbury, N.Y.). 30(8)
- Publication Year :
- 2020
-
Abstract
- Recent interest in exploiting machine learning for model-free prediction of chaotic systems focused on the time evolution of the dynamical variables of the system as a whole, which include both amplitude and phase. In particular, in the framework based on reservoir computing, the prediction horizon as determined by the largest Lyapunov exponent is often short, typically about five or six Lyapunov times that contain approximately equal number of oscillation cycles of the system. There are situations in the real world where the phase information is important, such as the ups and downs of species populations in ecology, the polarity of a voltage variable in an electronic circuit, and the concentration of certain chemical above or below the average. Using classic chaotic oscillators and a chaotic food-web system from ecology as examples, we demonstrate that reservoir computing can be exploited for long-term prediction of the phase of chaotic oscillators. The typical prediction horizon can be orders of magnitude longer than that with predicting the entire variable, for which we provide a physical understanding. We also demonstrate that a properly designed reservoir computing machine can reliably sense phase synchronization between a pair of coupled chaotic oscillators with implications to the design of the parallel reservoir scheme for predicting large chaotic systems.
- Subjects :
- Lyapunov function
Computer science
Phase (waves)
Chaotic
General Physics and Astronomy
Lyapunov exponent
Machine learning
computer.software_genre
01 natural sciences
010305 fluids & plasmas
Machine Learning
symbols.namesake
0103 physical sciences
010306 general physics
Mathematical Physics
Oscillation
business.industry
Applied Mathematics
Reservoir computing
Statistical and Nonlinear Physics
Phase synchronization
Orders of magnitude (time)
Nonlinear Dynamics
symbols
Artificial intelligence
Electronics
business
computer
Subjects
Details
- ISSN :
- 10897682
- Volume :
- 30
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Chaos (Woodbury, N.Y.)
- Accession number :
- edsair.doi.dedup.....f1ec8fec4c0dbf586e9648fe0ed77a17