Research Article
An Analysis Model of Automobile Running State Based on Neural Network
@INPROCEEDINGS{10.1007/978-3-030-77428-8_7, author={Jie Yan and Xinxin Guan and Qingtao Zeng and Chufeng Zhou and Yeli Li and Fucheng You}, title={An Analysis Model of Automobile Running State Based on Neural Network}, proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 15th EAI International Conference, TridentCom 2020, Virtual Event, November 13, 2020, Proceedings}, proceedings_a={TRIDENTCOM}, year={2021}, month={5}, keywords={Automobile running state Self-organizing mapping neural networks K-means model Two-segment clustering}, doi={10.1007/978-3-030-77428-8_7} }- Jie Yan
Xinxin Guan
Qingtao Zeng
Chufeng Zhou
Yeli Li
Fucheng You
Year: 2021
An Analysis Model of Automobile Running State Based on Neural Network
TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-77428-8_7
Abstract
A reasonable design of the operating condition curve of automobile running state is conducive to improving the credibility of the government, so it is more and more important to formulate a test condition that reflects the actual road driving conditions in China. The actual fuel consumption is very different from the regulatory certification results. In order to construct the model mainly by two-segment clustering, the initial clustering of the processed data is carried out by self-organizing mapping neural network, and the cluster number and clustering center are obtained to solve the problem of poor convergence in the K-means model in the early stage. In view of the construction of the operating condition curve of the driving characteristics of light vehicles in a city, the data pre-processing, the extraction of motion fragments and the construction of the driving conditions of a car are to be provided for the driving data set of the same vehicle in a city.
