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Predicting dynamical systems with echo state networks with different topology

Räth, Christoph (2019) Predicting dynamical systems with echo state networks with different topology. International Workshop on Complex Systems and Networks 2019, 23.-26. Sept. 2019, Berlin, Deutschland. (Unpublished)

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Abstract

The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, the so-called echo state networks (ESN) turned out to be a very promising approach especially for the reproduction of the long-term properties of the system. The heart of ESN is a network of nodes that is fed with input data and is connected with an output layer. So far only random Erdös-Renyi networks are used. However, there is a variety of conceivable other network topologies that may have an influence on the results. As a first step, we statistically analyze the quality of prediction - 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 for random, small world and scale-free networks. Using the Lorenz and the Rössler system we find significant variations of the results. The longterm prediction is worse for the scale-free network, where the differences between the network types are more pronounced in the Rössler system. Random and scale-free networks perform similar in both cases with slight advantages for the small world network. Our results suggest that the network topology has significant influence on the performance of ESN. Studying the results for different networks in detail also gives new insights about the complexity of the underlying dynamical system.

Item URL in elib:https://elib.dlr.de/129923/
Document Type:Conference or Workshop Item (Speech)
Title:Predicting dynamical systems with echo state networks with different topology
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Räth, ChristophChristoph.Raeth (at) dlr.deUNSPECIFIED
Date:2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Unpublished
Keywords:Machine Learning, Reservoir Computing, Networks, Complex Systems, Prediction
Event Title:International Workshop on Complex Systems and Networks 2019
Event Location:Berlin, Deutschland
Event Type:international Conference
Event Dates:23.-26. Sept. 2019
Organizer:Humboldt Universität
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:28 Oct 2019 07:05
Last Modified:28 Oct 2019 07:05

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