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

被引:35
作者
Hou, Jue [1 ]
Yao, Dongwei [1 ]
Wu, Feng [1 ]
Shen, Junhao [1 ]
Chao, Xiangyun [1 ]
机构
[1] Zhejiang Univ, Coll Energy Engn, Power Machinery & Vehicular Engn Inst, Hangzhou 310027, Zhejiang, Peoples R China
关键词
Training; Artificial neural networks; Neurons; Predictive models; Optimization; Adaptation models; Real-time systems; Radial basis function neural network; velocity prediction; predictive energy management strategy; neural network structure determination; ENERGY MANAGEMENT STRATEGY; SPEED PREDICTION;
D O I
10.1109/TVT.2021.3063483
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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%.
引用
收藏
页码:3113 / 3122
页数:10
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