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Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication

Leisheng Jin Orcid Logo, Zhuo Liu, Lijie Li Orcid Logo

EURASIP Journal on Advances in Signal Processing, Volume: 2022, Issue: 1

Swansea University Author: Lijie Li Orcid Logo

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DOI (Published version): 10.1186/s13634-022-00893-0

Abstract

In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchron...

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Published in: EURASIP Journal on Advances in Signal Processing
ISSN: 1687-6180
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60772
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first_indexed 2022-08-24T12:47:11Z
last_indexed 2023-01-13T19:21:09Z
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spelling 2022-08-24T13:50:06.3996841 v2 60772 2022-08-08 Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication ed2c658b77679a28e4c1dcf95af06bd6 0000-0003-4630-7692 Lijie Li Lijie Li true false 2022-08-08 EEEG In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications. Journal Article EURASIP Journal on Advances in Signal Processing 2022 1 Springer Science and Business Media LLC 1687-6180 Reservoir computing, Machine learning, Synchronization, Chaos signal, FPGA, Secure communication 28 7 2022 2022-07-28 10.1186/s13634-022-00893-0 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University The authors acknowledge the support from China Postdoctoral Science Foundation (No. 2019T120447). 2022-08-24T13:50:06.3996841 2022-08-08T09:17:09.1649209 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Leisheng Jin 0000-0002-0591-8211 1 Zhuo Liu 2 Lijie Li 0000-0003-4630-7692 3 60772__25011__de00f97e49ae4d35b5601c4af07cde94.pdf 60772_VoR.pdf 2022-08-24T13:48:02.7648280 Output 4320039 application/pdf Version of Record true © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
spellingShingle Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
Lijie Li
title_short Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
title_full Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
title_fullStr Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
title_full_unstemmed Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
title_sort Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication
author_id_str_mv ed2c658b77679a28e4c1dcf95af06bd6
author_id_fullname_str_mv ed2c658b77679a28e4c1dcf95af06bd6_***_Lijie Li
author Lijie Li
author2 Leisheng Jin
Zhuo Liu
Lijie Li
format Journal article
container_title EURASIP Journal on Advances in Signal Processing
container_volume 2022
container_issue 1
publishDate 2022
institution Swansea University
issn 1687-6180
doi_str_mv 10.1186/s13634-022-00893-0
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 1
active_str 0
description In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications.
published_date 2022-07-28T04:19:09Z
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score 11.013596

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