elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Assessing and improving the replication of chaotic attractors by means of reservoir computing

Haluszczynski, Alexander and Räth, Christoph and Laut, Ingo and Schwabe, Mierk (2019) Assessing and improving the replication of chaotic attractors by means of reservoir computing. DPG Frühjahrestagung, 1.-5. April 2019, Regensburg.

Full text not available from this repository.

Abstract

The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, the so-called "reservoir computing" method turned out to be a very promising approach especially for the reproduction of the long-term properties of the system [1]. Yet, a thorough statistical analysis of the forecast results is missing. So far the standard approach is to use purely random Erdös-Renyi networks for the reservoir in the model. It is obvious that there is a variety of conceivable network topologies that may have an influence on the results. Using the Lorenz System we statistically analyze the quality of predicition for different parametrizations - both the exact short term prediction as well as the reproduction of the long-term properties of the system as estimated by the correlation dimension and largest Lyapunov exponent. We find that both short and longterm predictions vary significantly. Thus special care must be taken in selecting the good predictions. We investigate the benefit of using different network topologies such as Small World or Scale Free networks and show which effect they have on the prediction quality. Our results suggest that the overall performance is best for small world networks. [1] J. Pathak et al., Chaos, 27, 121102 (2017)

Item URL in elib:https://elib.dlr.de/127109/
Document Type:Conference or Workshop Item (Speech)
Title:Assessing and improving the replication of chaotic attractors by means of reservoir computing
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Haluszczynski, AlexanderLMUUNSPECIFIED
Räth, ChristophChristoph.Raeth (at) dlr.deUNSPECIFIED
Laut, IngoIngo.Laut (at) dlr.deUNSPECIFIED
Schwabe, MierkMierk.Schwabe (at) dlr.dehttps://orcid.org/0000-0001-6565-5890
Date:April 2019
Refereed publication:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Nonlinear time series analysis, strange attractor, machine learning, prediction
Event Title:DPG Frühjahrestagung
Event Location:Regensburg
Event Type:international Conference
Event Dates:1.-5. April 2019
Organizer:DPG
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Research under Space Conditions
DLR - Research area:Raumfahrt
DLR - Program:R FR - Forschung unter Weltraumbedingungen
DLR - Research theme (Project):R - Komplexe Plasmen / Data analysis
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Materials Physics in Space > Research Group Complex Plasma
Deposited By: Räth, Christoph
Deposited On:15 Apr 2019 08:02
Last Modified:29 Jul 2019 07:49

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.