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

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS(2021)

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摘要
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.
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关键词
Machine learning,nonlinear dynamics,reservoir computing
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