File Download

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

장봉수

Jang, Bongsoo
Computational Mathematical Science Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Reservoir concatenation and the spectrum distribution of concatenated reservoir state matrices

Author(s)
Choi, JaesungPark EonyoungJang, BongsooKim, Yunho
Issued Date
2023-11
DOI
10.1063/5.0150113
URI
https://scholarworks.unist.ac.kr/handle/201301/66194
Citation
AIP ADVANCES, v.13, no.11, pp.115110
Abstract
Reservoir computing, one of the state-of-the-art machine learning architectures, processes time-series data generated by dynamical systems. Nevertheless, we have realized that reservoir computing with the conventional single-reservoir structure suffers from capacity saturation. This leads to performance stagnation in practice. Therefore, we propose an extended reservoir computing architecture called reservoir concatenation to further delay such stagnation. Not only do we provide training error analysis and test error comparison of reservoir concatenation, but we also propose a crucial measure, which is the trace associated with a reservoir state matrix, that explains the level of responsiveness to reservoir concatenation. Two reservoir dynamics are compared in detail, one by using the echo state network and the other by using a synchronization model called an explosive Kuramoto model. The distinct eigenvalue distributions of the reservoir state matrices from the two models are well reflected in the trace values that are shown to account for the different reservoir capacity behaviors, determining the different levels of responsiveness.
Publisher
American Institute of Physics Inc.
ISSN
2158-3226

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.