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Found 7 papers, 2 papers with code
Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction
Reservoir computing is a machine learning framework that has been shown to be able to replicate the chaotic attractor, including the fractal dimension and the entire Lyapunov spectrum, of the dynamical system on which it is trained.
Reservoir Computing with Noise
For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.
Time-shift selection for reservoir computing using a rank-revealing QR algorithm
Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems.
Time Shifts to Reduce the Size of Reservoir Computers
Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers.
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.
Critical Switching in Globally Attractive Chimeras
We report on a new type of chimera state that attracts almost all initial conditions and exhibits power-law switching behavior in networks of coupled oscillators.
Disordered Systems and Neural Networks Dynamical Systems Adaptation and Self-Organizing Systems Chaotic Dynamics Pattern Formation and Solitons
Topological Control of Synchronization Patterns: Trading Symmetry for Stability
Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics.
Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Chaotic Dynamics Pattern Formation and Solitons
