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...

Full description

Saved in:
Bibliographic Details
Published in:Advances in engineering software (1992) Vol. 173; p. 103199
Main Authors: Chen, ZY, Wang, Ruei-Yuan, Jiang, Rong, Chen, Timothy
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2022
Subjects:
Artificial intelligence
Evolved control
Gray DGM (2,1) model
Mechanical Elastic Vehicle Wheel (MEVW)
Nonlinear control
Evolved control
Mechanical Elastic Vehicle Wheel (MEVW)
Artificial intelligence
Gray DGM (2,1) model
Nonlinear control
ISSN:0965-9978
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•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).
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2022.103199