Online Vehicle Velocity Prediction Using an Adaptive Radial Basis Function Neural Network

In order to improve the performance of predictive energy management strategies (PEMS), a novel neural network based vehicle velocity prediction strategy (NN-VVP) was proposed. First, an online trained radial basis function neural network (RBF-NN) with a fixed structure was adopted to build online ve...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-04, Vol.70 (4), p.3113-3122
Main Authors: Hou, Jue, Yao, Dongwei, Wu, Feng, Shen, Junhao, Chao, Xiangyun
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Adaptive structures
Artificial neural networks
Energy management
neural network structure determination
Neural networks
Neurons
Optimization
Performance prediction
predictive energy management strategy
Predictive models
Radial basis function
Radial basis function neural network
Real-time systems
Training
Velocity
velocity prediction
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Summary:In order to improve the performance of predictive energy management strategies (PEMS), a novel neural network based vehicle velocity prediction strategy (NN-VVP) was proposed. First, an online trained radial basis function neural network (RBF-NN) with a fixed structure was adopted to build online vehicle velocity prediction (VVP) model. The influence of order and width of RBF-NN on the online prediction accuracy was studied in depth, it was found that RBF-NN with a fixed structure was not always suitable for the overall online prediction process. Then, by introducing a neural network structure determination method (SDM) with the Akaike Information Criterion (AIC), an adaptive RBF-NN which adjust structure in real time was designed to perform online VVP to further improve the prediction accuracy. Simulation results indicate that, the VVP strategy proposed in this paper predicts the future vehicle velocity with acceptable accuracy. Compared with the fixed structure, the RBF-NN with an adaptive structure significantly improve the prediction accuracy by approximately 63.2%, 70.4%, and 71.1%.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3063483