Neural ordinary differential gray algorithm to forecasting nonlinear systems
•A novel approach that the control of NODG algorithm can be realized.•The system is gloabally dominated by regulating the novel algorithm design.•The simulation is demonstrated to improve the ride comfort of vehicles. Due to the feasibility of the gray model for predicting time series with small sam...
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| Published in: | Advances in engineering software (1992) Vol. 173; p. 103199 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2022
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| ISSN: | 0965-9978 |
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| Abstract | •A novel approach that the control of NODG algorithm can be realized.•The system is gloabally dominated by regulating the novel algorithm design.•The simulation is demonstrated to improve the ride comfort of vehicles.
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 (NODE), which is named the NODGM (Neural Based Ordinary Differential gray Mode). This mode permits the forecasting approximation 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 NODGM can provide comprehensive applications as well as learning the properties of distinct data specimens. In order to acquire a better model which has highly predictive efficiency, afterwards, this study trains the model by NODGM meanwhile 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 comprehensive spreads the utilize extent of Mechanical Elastic Vehicle Wheel (MEVW). |
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| AbstractList | •A novel approach that the control of NODG algorithm can be realized.•The system is gloabally dominated by regulating the novel algorithm design.•The simulation is demonstrated to improve the ride comfort of vehicles.
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 (NODE), which is named the NODGM (Neural Based Ordinary Differential gray Mode). This mode permits the forecasting approximation 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 NODGM can provide comprehensive applications as well as learning the properties of distinct data specimens. In order to acquire a better model which has highly predictive efficiency, afterwards, this study trains the model by NODGM meanwhile 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 comprehensive spreads the utilize extent of Mechanical Elastic Vehicle Wheel (MEVW). |
| ArticleNumber | 103199 |
| Author | Chen, Timothy Wang, Ruei-Yuan Chen, ZY Jiang, Rong |
| Author_xml | – sequence: 1 givenname: ZY surname: Chen fullname: Chen, ZY organization: School of Science, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China – sequence: 2 givenname: Ruei-Yuan surname: Wang fullname: Wang, Ruei-Yuan email: 640053@gdupt.edu.cn organization: School of Science, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China – sequence: 3 givenname: Rong surname: Jiang fullname: Jiang, Rong organization: School of Science, Guangdong University of Petrochemical Technology, Maoming 525000, Guangdong, China – sequence: 4 givenname: Timothy surname: Chen fullname: Chen, Timothy email: t13929751005@gmail.com organization: California Institute of Technology, Pasadena, CA 91125, USA |
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| Keywords | Evolved control Mechanical Elastic Vehicle Wheel (MEVW) Artificial intelligence Gray DGM (2,1) model Nonlinear control |
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