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

Urheber*innen

Köster,  Felix
External Organizations;

/persons/resource/yanchuk

Yanchuk,  Serhiy
Potsdam Institute for Climate Impact Research;

Lüdge,  Kathy
External Organizations;

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Zitation

Köster, F., Yanchuk, S., Lüdge, K. (2022 online): Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics. - IEEE Transactions on Neural Networks and Learning Systems.
https://doi.org/10.1109/TNNLS.2022.3220532


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_27572
Zusammenfassung
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.