Claudio Gallicchio and Andrea Ceni, Reservoir Memory Networks, ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024.
Abstract: We introduce Reservoir Memory Networks (RMNs), a novel class of Reservoir Computing (RC) models that integrate a linear memory cell with a non-linear reservoir to enhance long-term information retention. We explore various configurations of the memory cell using orthogonal circular shift matrices and Legendre polynomials, alongside non-linear reservoirs configured as in Echo State Networks and Euler State Networks. Experimental results demonstrate the substantial benefits of RMNs in time-series classification tasks, highlighting their potential for advancing RC applications in areas requiring robust temporal processing.
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