Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

doi:10.1109/TNNLS.2022.3220532

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

2024-06-01

6

OA

AI

DOI：10.1109/TNNLS.2022.3220532

Original

Original request for help

Save

Abstract

En 中文

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.

Keywords:

Machine learning

nonlinear dynamics

reservoir computing

Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

Abstract

Journal

Organization