Reservoir computing (RC) systems can efficiently forecast chaotic time series using the
nonlinear dynamical properties of an artificial neural network of random connections. The …

Abstract Next Generation Reservoir Computing (NG-RC) is a modern class of model-free
machine learning that enables an accurate forecasting of time series data generated by …

Reservoir computing (RC) is an efficient artificial neural network for model-free prediction
and analysis of dynamical systems time series. As a data-based method, the capacity of RC …

Next-generation reservoir computing is a machine-learning approach that has been recently
proposed as an effective method for predicting the dynamics of chaotic systems. So far, this …

Abstract Machine learning methods based on reservoir computing have shown great
potential in predicting the dynamics of complex systems. However, the learning and …

Reservoir computing (RC) possesses a simple architecture and high energy efficiency for
time‐series data analysis through machine learning algorithms. To date, RC has evolved …

Reconstructing attractors of airfoil systems from observations facilitates understanding of
aeroelasticity, especially the onset of flutter. However, it is generally difficult due to …

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir
computing (RC), has garnered significant attention in the field of time series prediction in …