Evolutionary Echo State Network: evolving reservoirs in the Fourier space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10253425" target="_blank" >RIV/61989100:27240/22:10253425 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9892892" target="_blank" >https://ieeexplore.ieee.org/document/9892892</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN55064.2022.9892892" target="_blank" >10.1109/IJCNN55064.2022.9892892</a>
Alternative languages
Result language
angličtina
Original language name
Evolutionary Echo State Network: evolving reservoirs in the Fourier space
Original language description
The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have recently received significant attention due to their remarkable success in the modeling of non-linear dynamical systems. The reservoir is randomly connected with fixed weights that don't change in the learning process. Only the weights from reservoir to output are trained. Since the reservoir is fixed during the training procedure, we may wonder if the computational power of the recurrent structure is fully harnessed. In this article, we propose a new computational model of the ESN type, that represents the reservoir weights in the Fourier space and performs a fine-tuning of these weights applying genetic algorithms in the frequency domain. The main interest is that this procedure will work in a much smaller space compared to the classical ESN, thus providing a dimensionality reduction transformation of the initial method. The proposed technique allows us to exploit the benefits of the large recurrent structure avoiding the training problems of gradient-based method. We provide a detailed experimental study that demonstrates the good performances of our approach with well-known chaotic systems and real-world data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
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)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
ISBN
978-1-72818-671-9
ISSN
2161-4393
e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IEEE
Place of publication
NEW YORK
Event location
Padua
Event date
Jul 18, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000867070908004