State prediction of MR system by VMD-GRNN based on fractal dimension
Taking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms, based on time series, time series Auto-Regressive (AR) model coefficients, time series box dimensions, and Variational...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-12-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132221145899 |
| _version_ | 1797978327923818496 |
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| author | Chen Yi-ze Chen Qing-tang |
| author_facet | Chen Yi-ze Chen Qing-tang |
| author_sort | Chen Yi-ze |
| collection | DOAJ |
| description | Taking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms, based on time series, time series Auto-Regressive (AR) model coefficients, time series box dimensions, and Variational Modal Decomposition (VMD) box dimensions, are designed. Moreover, four Back Propagation Neural Network (BPNN)comparative prediction algorithms, based on the four previous parameters, are also designed. These eight algorithms are applied to predict vibration damping efficiency of the system. The prediction results show that, compared to the BPNN prediction algorithm, the corresponding four GRNN prediction algorithms have the advantages of strong self-learning ability, fast convergence speed, high prediction accuracy, and stable prediction results. Among the eight prediction algorithms, the GRNN prediction algorithm, based on VMD box dimension, forecasts the results with good stability, better self-learning ability, and higher computing speed, which can maximize the prediction accuracy of the system, the minimum prediction error can reach 1.9049% when the parameters K = 4, N = 33 , and Spread = 0.601. To sum up, through parameter optimization, the optimal parameter combination scheme of GRNN prediction algorithm, based on VMD box dimension, is obtained, and the best prediction effect is achieved. |
| first_indexed | 2024-04-11T05:21:15Z |
| format | Article |
| id | doaj.art-08d2f073eddf41e4a5785f283b4e4114 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-04-11T05:21:15Z |
| publishDate | 2022-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-08d2f073eddf41e4a5785f283b4e41142022-12-23T19:05:11ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402022-12-011410.1177/16878132221145899State prediction of MR system by VMD-GRNN based on fractal dimensionChen Yi-ze0Chen Qing-tang1School of management, Hefei University of Technology, Anhui, Hefei, ChinaCollege of Electromechanical and Information Engineering, Putian University, Fujian, Putian, ChinaTaking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms, based on time series, time series Auto-Regressive (AR) model coefficients, time series box dimensions, and Variational Modal Decomposition (VMD) box dimensions, are designed. Moreover, four Back Propagation Neural Network (BPNN)comparative prediction algorithms, based on the four previous parameters, are also designed. These eight algorithms are applied to predict vibration damping efficiency of the system. The prediction results show that, compared to the BPNN prediction algorithm, the corresponding four GRNN prediction algorithms have the advantages of strong self-learning ability, fast convergence speed, high prediction accuracy, and stable prediction results. Among the eight prediction algorithms, the GRNN prediction algorithm, based on VMD box dimension, forecasts the results with good stability, better self-learning ability, and higher computing speed, which can maximize the prediction accuracy of the system, the minimum prediction error can reach 1.9049% when the parameters K = 4, N = 33 , and Spread = 0.601. To sum up, through parameter optimization, the optimal parameter combination scheme of GRNN prediction algorithm, based on VMD box dimension, is obtained, and the best prediction effect is achieved.https://doi.org/10.1177/16878132221145899 |
| spellingShingle | Chen Yi-ze Chen Qing-tang State prediction of MR system by VMD-GRNN based on fractal dimension Advances in Mechanical Engineering |
| title | State prediction of MR system by VMD-GRNN based on fractal dimension |
| title_full | State prediction of MR system by VMD-GRNN based on fractal dimension |
| title_fullStr | State prediction of MR system by VMD-GRNN based on fractal dimension |
| title_full_unstemmed | State prediction of MR system by VMD-GRNN based on fractal dimension |
| title_short | State prediction of MR system by VMD-GRNN based on fractal dimension |
| title_sort | state prediction of mr system by vmd grnn based on fractal dimension |
| url | https://doi.org/10.1177/16878132221145899 |
| work_keys_str_mv | AT chenyize statepredictionofmrsystembyvmdgrnnbasedonfractaldimension AT chenqingtang statepredictionofmrsystembyvmdgrnnbasedonfractaldimension |