Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of …

Mar 6, 2024 · Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively …

1 day ago · For this reason, reservoir computing, where one employs high-dimensional recurrent networks and trains only the final layer, is widely used in this context. In this chapter, I …

Dec 12, 2024 · In this chapter, I introduce the basic concepts of reservoir computing. Moreover, I present some important physical implementations coming from electronics, photonics, …

Reservoir computing (RC) is defined as a machine learning paradigm that processes information from dynamical systems using observed time series data, requiring minimal training datasets …

Mar 18, 2024 · Reservoir computing, a form of artificial intelligence, relies on recurrent neural networks that mimic the information storage and processing mechanisms of the human brain.

Reservoir computing is a computational framework derived from recurrent neural network theory. It works by mapping input signals into higher dimensional computational spaces through the …

Any reservoir must satisfy two properties: It must be capable of storing information. These two properties endow a reservoir with a recurrent nature, i.e., a state to store information over …

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

Conceptually the idea behind reservoir computing is that performing a complex non-linear transformation on data can produce a high dimensional solution space on which non-trivial …