Using reservoir computers to distinguish chaotic signals

Abstract

Several recent papers have shown that reservoir computers are useful for analyzing and predicting dynamical systems. Reservoir computers have also been shown to be useful for various classification problems. In this work, a reservoir computer is used to identify one out of the 19 different Sprott systems. An advantage of reservoir computers for this problem is that no embedding is necessary. Some guidance on choosing the reservoir computer parameters is given. The dependance on number of points, number of reservoir nodes and noise in identifying the Sprott systems is explored.

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• Adaptable Hamiltonian neural networks
• Learning Hamiltonian dynamics by reservoir computer
• Learning strange attractors with reservoir systems
• Reservoir Computing with Noise
• Catch-22s of reservoir computing
• Finding nonlinear system equations and complex network structures from data: a sparse optimization approach
• Reducing network size and improving prediction stability of reservoir computing
• The Echo Index and multistability in input-driven recurrent neural networks
• Dynamical analysis of a parameter-aware reservoir computer
• Role of assortativity in predicting burst synchronization using echo state network
• Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius
• Stability of exoplanetary systems retrieved from scalar time series
• Long-term prediction of chaotic systems with recurrent neural networks
• Deep Chaos Synchronization
• Reservoir observer enhanced with residual calibration and attention mechanism