Deep learning prediction of amplitude death
Abstract Affected by parameter drift and coupling organization, nonlinear dynamical systems exhibit suppressed oscillations. This phenomenon is called amplitude death. In various complex systems, amplitude death is a typical critical phenomenon, which may lead to the functional collapse of the syste...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-11-01
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| Series: | Autonomous Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43684-022-00044-0 |
| _version_ | 1797983934078779392 |
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| author | Pengcheng Ji Tingyi Yu Yaxuan Zhang Wei Gong Qingyun Yu Li Li |
| author_facet | Pengcheng Ji Tingyi Yu Yaxuan Zhang Wei Gong Qingyun Yu Li Li |
| author_sort | Pengcheng Ji |
| collection | DOAJ |
| description | Abstract Affected by parameter drift and coupling organization, nonlinear dynamical systems exhibit suppressed oscillations. This phenomenon is called amplitude death. In various complex systems, amplitude death is a typical critical phenomenon, which may lead to the functional collapse of the system. Therefore, an important issue is how to effectively predict critical phenomena based on the data in the system oscillation state. This paper proposes an enhanced Informer model to predict amplitude death. The model employs an attention mechanism to capture the long-range associations of the system time series and tracks the effect of parameter drift on the system dynamics through an accompanying parameter input channel. The experimental results based on the coupled Rössler and Lorentz systems show that the enhanced informer has higher prediction accuracy and longer effective prediction distance than the original algorithm and can predict the amplitude death of a system. |
| first_indexed | 2024-04-11T06:54:57Z |
| format | Article |
| id | doaj.art-cbd0b88aa1ea4b87b790e85e3446218a |
| institution | Directory Open Access Journal |
| issn | 2730-616X |
| language | English |
| last_indexed | 2024-04-11T06:54:57Z |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj.art-cbd0b88aa1ea4b87b790e85e3446218a2022-12-22T04:39:04ZengSpringerAutonomous Intelligent Systems2730-616X2022-11-012111110.1007/s43684-022-00044-0Deep learning prediction of amplitude deathPengcheng Ji0Tingyi Yu1Yaxuan Zhang2Wei Gong3Qingyun Yu4Li Li5College of Electronics and Information Engineering, Tongji UniversityCollege of Electronics and Information Engineering, Tongji UniversityCollege of Electronics and Information Engineering, Tongji UniversityCollege of Electronics and Information Engineering, Tongji UniversityCollege of Electronics and Information Engineering, Tongji UniversityCollege of Electronics and Information Engineering, Tongji UniversityAbstract Affected by parameter drift and coupling organization, nonlinear dynamical systems exhibit suppressed oscillations. This phenomenon is called amplitude death. In various complex systems, amplitude death is a typical critical phenomenon, which may lead to the functional collapse of the system. Therefore, an important issue is how to effectively predict critical phenomena based on the data in the system oscillation state. This paper proposes an enhanced Informer model to predict amplitude death. The model employs an attention mechanism to capture the long-range associations of the system time series and tracks the effect of parameter drift on the system dynamics through an accompanying parameter input channel. The experimental results based on the coupled Rössler and Lorentz systems show that the enhanced informer has higher prediction accuracy and longer effective prediction distance than the original algorithm and can predict the amplitude death of a system.https://doi.org/10.1007/s43684-022-00044-0Complex systemAmplitude deathBifurcation parameterAttention mechanism |
| spellingShingle | Pengcheng Ji Tingyi Yu Yaxuan Zhang Wei Gong Qingyun Yu Li Li Deep learning prediction of amplitude death Autonomous Intelligent Systems Complex system Amplitude death Bifurcation parameter Attention mechanism |
| title | Deep learning prediction of amplitude death |
| title_full | Deep learning prediction of amplitude death |
| title_fullStr | Deep learning prediction of amplitude death |
| title_full_unstemmed | Deep learning prediction of amplitude death |
| title_short | Deep learning prediction of amplitude death |
| title_sort | deep learning prediction of amplitude death |
| topic | Complex system Amplitude death Bifurcation parameter Attention mechanism |
| url | https://doi.org/10.1007/s43684-022-00044-0 |
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