Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 7712-7725 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 35 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2024 |
| Externally published | Yes |
Keywords
- Machine learning
- nonlinear dynamics
- reservoir computing