Evolutionary Echo State Network: A neuroevolutionary framework for time series prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253424" target="_blank" >RIV/61989100:27240/23:10253424 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494623004817?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494623004817?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.110463" target="_blank" >10.1016/j.asoc.2023.110463</a>

Result language

angličtina

Original language name

Evolutionary Echo State Network: A neuroevolutionary framework for time series prediction

Original language description

From one side, Evolutionary Algorithms have enabled enormous progress over the last years in the optimization field. They have been applied to a variety of problems, including optimization of Neural Networks&apos; architectures. On the other side, the Echo State Network (ESN) model has become increasingly popular in time series prediction, for instance when modeling chaotic sequences. The network has numerous hidden neurons forming a recurrent topology, so-called reservoir, which is fixed during the learning process. Initial reservoir design has mostly been made by human experts; as a consequence, it is prone to errors and bias, and it is a time consuming task. In this paper, we introduce an automatic general neuroevolutionary framework for ESNs, on which we develop a computational tool for evolving reservoirs, called EVOlutionary Echo State Network (EvoESN). To increase efficiency, we represent the large matrix of reservoir weights in the Fourier space, where we perform the evolutionary search strategy. This frequency space has major advantages compared with the original weight space. After updating the Fourier coefficients, we go back to the weight space and perform a conventional training phase for full setting the reservoir architecture. We analyze the evolutionary search employing genetic algorithms and particle swarm optimization, obtaining promising results with the latter over three well-known chaotic time series. The proposed framework leads fast to very good results compared with modern ESN models. Hence, this contribution positions an important family of recurrent systems in the promising neuroevolutionary domain.&amp; COPY; 2023 Elsevier B.V. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GF21-33574K" target="_blank" >GF21-33574K: Lifelong Machine Learning on Data Streams</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

144

Issue of the periodical within the volume

September

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

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UT code for WoS article

001054625900001

EID of the result in the Scopus database

2-s2.0-85162172245