Neural ordinary differential gray algorithm to forecasting nonlinear 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 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 spec-imens 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 effi-ciency, 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).

Automated summary

This study proposes a new gray prediction criterion based on the Neural Ordinary Differential Equation, which allows the forecasting approximation to be learned through a training process. It can be applied to different data specimens and achieves better predictive efficiency with the use of the Runge-Kutta method.