Double array system identification research based on LSTM neural network
The earthquake simulation shaking table array is an important experimental equipment with a wide range of applications in the field of earthquake engineering. To efficiently address the complex nonlinear problems associated with earthquake simulation shaking array systems, this paper proposes the id...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1475622/full |
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| _version_ | 1841556843367759872 |
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| author | Chunhua Gao Mingyang Wang Yifei Sima Zihan Yuan |
| author_facet | Chunhua Gao Mingyang Wang Yifei Sima Zihan Yuan |
| author_sort | Chunhua Gao |
| collection | DOAJ |
| description | The earthquake simulation shaking table array is an important experimental equipment with a wide range of applications in the field of earthquake engineering. To efficiently address the complex nonlinear problems associated with earthquake simulation shaking array systems, this paper proposes the identification of the earthquake simulation shaking array system using the Long Short-Term Memory (LSTM) algorithm. A dual array system model with flexible specimen connections is established, and this system is identified using the LSTM neural network. The LSTM neural network was validated for identifying the dual array closed-loop system of the earthquake simulation shaking table by using three natural waves and one artificial wave. The results demonstrated that the similarity between the predicted output and the theoretical output of the network identified by LSTM exceeded 0.999. This indicates that the algorithm can accurately reproduce the characteristics of the shaking table itself and shows good performance in time series prediction and data mining. References for earthquake simulation shaking array system experiments are provided. |
| format | Article |
| id | doaj-art-14f57d2400514d5d879777c8b97f40e9 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-14f57d2400514d5d879777c8b97f40e92025-01-07T05:23:48ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-01-011210.3389/fphy.2024.14756221475622Double array system identification research based on LSTM neural networkChunhua GaoMingyang WangYifei SimaZihan YuanThe earthquake simulation shaking table array is an important experimental equipment with a wide range of applications in the field of earthquake engineering. To efficiently address the complex nonlinear problems associated with earthquake simulation shaking array systems, this paper proposes the identification of the earthquake simulation shaking array system using the Long Short-Term Memory (LSTM) algorithm. A dual array system model with flexible specimen connections is established, and this system is identified using the LSTM neural network. The LSTM neural network was validated for identifying the dual array closed-loop system of the earthquake simulation shaking table by using three natural waves and one artificial wave. The results demonstrated that the similarity between the predicted output and the theoretical output of the network identified by LSTM exceeded 0.999. This indicates that the algorithm can accurately reproduce the characteristics of the shaking table itself and shows good performance in time series prediction and data mining. References for earthquake simulation shaking array system experiments are provided.https://www.frontiersin.org/articles/10.3389/fphy.2024.1475622/fullsystem identificationdual arrayLSTM neural networkshaking tabledeep learning |
| spellingShingle | Chunhua Gao Mingyang Wang Yifei Sima Zihan Yuan Double array system identification research based on LSTM neural network Frontiers in Physics system identification dual array LSTM neural network shaking table deep learning |
| title | Double array system identification research based on LSTM neural network |
| title_full | Double array system identification research based on LSTM neural network |
| title_fullStr | Double array system identification research based on LSTM neural network |
| title_full_unstemmed | Double array system identification research based on LSTM neural network |
| title_short | Double array system identification research based on LSTM neural network |
| title_sort | double array system identification research based on lstm neural network |
| topic | system identification dual array LSTM neural network shaking table deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1475622/full |
| work_keys_str_mv | AT chunhuagao doublearraysystemidentificationresearchbasedonlstmneuralnetwork AT mingyangwang doublearraysystemidentificationresearchbasedonlstmneuralnetwork AT yifeisima doublearraysystemidentificationresearchbasedonlstmneuralnetwork AT zihanyuan doublearraysystemidentificationresearchbasedonlstmneuralnetwork |