Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-25026
Publication type: | Article in scientific journal |
Type of review: | Peer review (publication) |
Title: | Abstract reservoir computing |
Authors: | Senn, Christoph Walter Kumazawa, Itsuo |
et. al: | No |
DOI: | 10.3390/ai3010012 10.21256/zhaw-25026 |
Published in: | AI |
Volume(Issue): | 3 |
Issue: | 1 |
Page(s): | 194 |
Pages to: | 210 |
Issue Date: | 10-Mar-2022 |
Publisher / Ed. Institution: | MDPI |
ISSN: | 2673-2688 |
Language: | English |
Subjects: | Reservoir computing; Echo state network; Recurrent neural network; Simulation; Robustness |
Subject (DDC): | 006: Special computer methods |
Abstract: | Noise of any kind can be an issue when translating results from simulations to the real world. We suddenly have to deal with building tolerances, faulty sensors, or just noisy sensor readings. This is especially evident in systems with many free parameters, such as the ones used in physical reservoir computing. By abstracting away these kinds of noise sources using intervals, we derive a regularized training regime for reservoir computing using sets of possible reservoir states. Numerical simulations are used to show the effectiveness of our approach against different sources of errors that can appear in real-world scenarios and compare them with standard approaches. Our results support the application of interval arithmetics to improve the robustness of mass-spring networks trained in simulations. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/25026 |
Fulltext version: | Published version |
License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
Departement: | School of Engineering |
Organisational Unit: | Institute of Applied Mathematics and Physics (IAMP) |
Appears in collections: | Publikationen School of Engineering |
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File | Description | Size | Format | |
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2022_Senn-Kumazawa_Abstract-Reservoir-Computing.pdf | 5.2 MB | Adobe PDF | View/Open |
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Senn, C. W., & Kumazawa, I. (2022). Abstract reservoir computing. Ai, 3(1), 194–210. https://doi.org/10.3390/ai3010012
Senn, C.W. and Kumazawa, I. (2022) ‘Abstract reservoir computing’, AI, 3(1), pp. 194–210. Available at: https://doi.org/10.3390/ai3010012.
C. W. Senn and I. Kumazawa, “Abstract reservoir computing,” AI, vol. 3, no. 1, pp. 194–210, Mar. 2022, doi: 10.3390/ai3010012.
SENN, Christoph Walter und Itsuo KUMAZAWA, 2022. Abstract reservoir computing. AI. 10 März 2022. Bd. 3, Nr. 1, S. 194–210. DOI 10.3390/ai3010012
Senn, Christoph Walter, and Itsuo Kumazawa. 2022. “Abstract Reservoir Computing.” Ai 3 (1): 194–210. https://doi.org/10.3390/ai3010012.
Senn, Christoph Walter, and Itsuo Kumazawa. “Abstract Reservoir Computing.” Ai, vol. 3, no. 1, Mar. 2022, pp. 194–210, https://doi.org/10.3390/ai3010012.
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