Detection of Chaos Using Reservoir Computing Approach
Detection of chaos in time series is of utmost importance in many scientific fields. Indeed, the presence of chaos and its significance, especially in multidimensional systems, plays an essential role in the control and analysis of such systems, and in their practical use in a variety of application...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9774326/ |
| _version_ | 1811255106627174400 |
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| author | Ali Rida Ismail Slavisa Jovanovic Sebastien Petit-Watelot Hassan Rabah |
| author_facet | Ali Rida Ismail Slavisa Jovanovic Sebastien Petit-Watelot Hassan Rabah |
| author_sort | Ali Rida Ismail |
| collection | DOAJ |
| description | Detection of chaos in time series is of utmost importance in many scientific fields. Indeed, the presence of chaos and its significance, especially in multidimensional systems, plays an essential role in the control and analysis of such systems, and in their practical use in a variety of applications. In this paper, we demonstrate a new methodology for the detection of chaos in time series using a reservoir computing (RC) paradigm called conceptor-driven network (ConDN). Case studies on the known chaotic attractors (i.e. Lorenz, Rossler, Chua) of integer (conventional) and non-integer (fractional-order) orders, as well as a physically simulated and designed spintronic device (NCVO) are used in this study to validate the proposed chaos detection approach. The proposed chaos detection approach is tested on clean and noisy time series of the mentioned attractors. It outperforms the 0–1 chaos detection test and the largest Lyapunov exponent (LLE) estimation approach especially in the high noise-level conditions. In addition, the proposed approach is capable of differentiating the time series generated by the systems whose dynamics is at the edge of chaos. The simplicity of use of the proposed chaos detection approach can be counted, as well, as one of its main advantages over traditional chaos detection methods. |
| first_indexed | 2024-04-12T17:18:54Z |
| format | Article |
| id | doaj.art-46d60782a8494edb8e34da798aee63ad |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T17:18:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-46d60782a8494edb8e34da798aee63ad2022-12-22T03:23:33ZengIEEEIEEE Access2169-35362022-01-0110526865269910.1109/ACCESS.2022.31749609774326Detection of Chaos Using Reservoir Computing ApproachAli Rida Ismail0https://orcid.org/0000-0001-9783-114XSlavisa Jovanovic1https://orcid.org/0000-0001-6459-7043Sebastien Petit-Watelot2Hassan Rabah3https://orcid.org/0000-0001-6334-3084Jean Lamour Institute (IJL), University of Lorraine, UMR 7198, Nancy, FranceJean Lamour Institute (IJL), University of Lorraine, UMR 7198, Nancy, FranceJean Lamour Institute (IJL), University of Lorraine, UMR 7198, Nancy, FranceJean Lamour Institute (IJL), University of Lorraine, UMR 7198, Nancy, FranceDetection of chaos in time series is of utmost importance in many scientific fields. Indeed, the presence of chaos and its significance, especially in multidimensional systems, plays an essential role in the control and analysis of such systems, and in their practical use in a variety of applications. In this paper, we demonstrate a new methodology for the detection of chaos in time series using a reservoir computing (RC) paradigm called conceptor-driven network (ConDN). Case studies on the known chaotic attractors (i.e. Lorenz, Rossler, Chua) of integer (conventional) and non-integer (fractional-order) orders, as well as a physically simulated and designed spintronic device (NCVO) are used in this study to validate the proposed chaos detection approach. The proposed chaos detection approach is tested on clean and noisy time series of the mentioned attractors. It outperforms the 0–1 chaos detection test and the largest Lyapunov exponent (LLE) estimation approach especially in the high noise-level conditions. In addition, the proposed approach is capable of differentiating the time series generated by the systems whose dynamics is at the edge of chaos. The simplicity of use of the proposed chaos detection approach can be counted, as well, as one of its main advantages over traditional chaos detection methods.https://ieeexplore.ieee.org/document/9774326/Chaos detectiontime seriesreservoir computingconceptors |
| spellingShingle | Ali Rida Ismail Slavisa Jovanovic Sebastien Petit-Watelot Hassan Rabah Detection of Chaos Using Reservoir Computing Approach IEEE Access Chaos detection time series reservoir computing conceptors |
| title | Detection of Chaos Using Reservoir Computing Approach |
| title_full | Detection of Chaos Using Reservoir Computing Approach |
| title_fullStr | Detection of Chaos Using Reservoir Computing Approach |
| title_full_unstemmed | Detection of Chaos Using Reservoir Computing Approach |
| title_short | Detection of Chaos Using Reservoir Computing Approach |
| title_sort | detection of chaos using reservoir computing approach |
| topic | Chaos detection time series reservoir computing conceptors |
| url | https://ieeexplore.ieee.org/document/9774326/ |
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