5 papers
Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
G Manjunath, Juan-Pablo Ortega, Alma van der Merwe
Reservoir computing typically relies on large, randomly generated reservoirs, enabling simple, often linear readouts. Over the past two decades, most constructions have exploited t…
Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?
Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega
The total memory capacity (MC) of linear recurrent neural networks (RNNs) has been proven to be equal to the rank of the corresponding Kalman controllability matrix, and it is almo…
Infinite-dimensional next-generation reservoir computing
Lyudmila Grigoryeva, Hannah Lim Jing Ting, Juan-Pablo Ortega
Next-generation reservoir computing (NG-RC) has attracted much attention due to its excellent performance in spatio-temporal forecasting of complex systems and its ease of implemen…
Forecasting causal dynamics with universal reservoirs
Lyudmila Grigoryeva, James Louw, Juan-Pablo Ortega
An iterated multistep forecasting scheme based on recurrent neural networks (RNN) is proposed for the time series generated by causal chains with infinite memory. This forecasting…
Data-driven cold starting of good reservoirs
Lyudmila Grigoryeva, Boumediene Hamzi, Felix P. Kemeth +4
Using short histories of observations from a dynamical system, a workflow for the post-training initialization of reservoir computing systems is described. This strategy is called…