Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive
development due to its exceptional efficacy and high performance in reconstruction and/or …

Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Reservoir computing (RC) is a novel approach to time series prediction using recurrent
neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a …

Reservoir computing (RC) is a powerful computational paradigm that allows high versatility
with cheap learning. While other artificial intelligence approaches need exhaustive …

Reservoir computing (RC) is a neural computing paradigm especially well-suited for
learning dynamical systems by leveraging an untrained reservoir layer, providing high …

Reservoir computing (RC) is gaining traction in several signal processing domains, owing to
its non-linear stateful computation, spatiotemporal encoding, and reduced training …

Reservoir Computing (RC) is an umbrella term for adaptive computational paradigms that
rely on an excitable dynamical system, also called the" reservoir." The paradigms have been …

Reservoir computing (RC) has attracted a lot of attention in the field of machine learning
because of its promising performance in a broad range of applications. However, it is difficult …

Reservoir Computing: Theory, Physical Implementations, and Applications is the first
comprehensive book about reservoir computing (RC). RC was introduced in the early 2000s …