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Found 5 papers, 2 papers with code
Optimizing time-shifts for reservoir computing using a rank-revealing QR algorithm
Reservoir computing is a recurrent neural network paradigm in which only the output layer is trained.
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
