Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-25026| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Abstract reservoir computing |
| Autor/-in: | Senn, Christoph Walter Kumazawa, Itsuo |
| et. al: | No |
| DOI: | 10.3390/ai3010012 10.21256/zhaw-25026 |
| Erschienen in: | AI |
| Band(Heft): | 3 |
| Heft: | 1 |
| Seite(n): | 194 |
| Seiten bis: | 210 |
| Erscheinungsdatum: | 10-Mär-2022 |
| Verlag / Hrsg. Institution: | MDPI |
| ISSN: | 2673-2688 |
| Sprache: | Englisch |
| Schlagwörter: | Reservoir computing; Echo state network; Recurrent neural network; Simulation; Robustness |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Angewandte Mathematik und Physik (IAMP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_Senn-Kumazawa_Abstract-Reservoir-Computing.pdf | 5.2 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.