Neural ordinary differential gray algorithm to forecasting models of controlled systems
Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural or...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-07-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806231171244 |
| _version_ | 1797776106739204096 |
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| author | ZY Chen YH Meng Rong Jiang Ruei-Yuan Wang Timothy Chen |
| author_facet | ZY Chen YH Meng Rong Jiang Ruei-Yuan Wang Timothy Chen |
| author_sort | ZY Chen |
| collection | DOAJ |
| description | Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel. |
| first_indexed | 2024-03-12T22:45:13Z |
| format | Article |
| id | doaj.art-70e46032a66b4bcf9514efcd2b7d3ea7 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-03-12T22:45:13Z |
| publishDate | 2023-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-70e46032a66b4bcf9514efcd2b7d3ea72023-07-21T06:03:34ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142023-07-012010.1177/17298806231171244Neural ordinary differential gray algorithm to forecasting models of controlled systemsZY Chen0YH Meng1Rong Jiang2Ruei-Yuan Wang3Timothy Chen4 Sch Sci, Guangdong University of Petrochemical Technology, Guangdong, Peoples Republic of China Sch Sci, Guangdong University of Petrochemical Technology, Guangdong, Peoples Republic of China Sch Sci, Guangdong University of Petrochemical Technology, Guangdong, Peoples Republic of China Sch Sci, Guangdong University of Petrochemical Technology, Guangdong, Peoples Republic of China Caltech, CA, USADue to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel.https://doi.org/10.1177/17298806231171244 |
| spellingShingle | ZY Chen YH Meng Rong Jiang Ruei-Yuan Wang Timothy Chen Neural ordinary differential gray algorithm to forecasting models of controlled systems International Journal of Advanced Robotic Systems |
| title | Neural ordinary differential gray algorithm to forecasting models of controlled systems |
| title_full | Neural ordinary differential gray algorithm to forecasting models of controlled systems |
| title_fullStr | Neural ordinary differential gray algorithm to forecasting models of controlled systems |
| title_full_unstemmed | Neural ordinary differential gray algorithm to forecasting models of controlled systems |
| title_short | Neural ordinary differential gray algorithm to forecasting models of controlled systems |
| title_sort | neural ordinary differential gray algorithm to forecasting models of controlled systems |
| url | https://doi.org/10.1177/17298806231171244 |
| work_keys_str_mv | AT zychen neuralordinarydifferentialgrayalgorithmtoforecastingmodelsofcontrolledsystems AT yhmeng neuralordinarydifferentialgrayalgorithmtoforecastingmodelsofcontrolledsystems AT rongjiang neuralordinarydifferentialgrayalgorithmtoforecastingmodelsofcontrolledsystems AT rueiyuanwang neuralordinarydifferentialgrayalgorithmtoforecastingmodelsofcontrolledsystems AT timothychen neuralordinarydifferentialgrayalgorithmtoforecastingmodelsofcontrolledsystems |