CNN and KPCA-Based Automated Feature Extraction for Real Time Driving Pattern Recognition

CNN and KPCA-Based Automated Feature Extraction for Real Time Driving Pattern Recognition

Driving conditions greatly affect the energy control and the fuel economy of a hybrid electric vehicle (HEV). In this paper, an automated feature extraction scheme based on convolution neural networks (CNNs) and Kernel PCA (KPCA) for real time driving pattern recognition (RTDPR) is proposed in order...

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Bibliographic Details
Main Authors: Liang Xie, Jili Tao, Qianni Zhang, Huiyu Zhou
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Convolution neural network
driving pattern recognition
feature selection
kernel principle component analysis
Online Access:https://ieeexplore.ieee.org/document/8822444/

Internet

https://ieeexplore.ieee.org/document/8822444/

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