2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific) 2014
DOI: 10.1109/itec-ap.2014.6940693 View full text |Buy / Rent full text
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“…As shown in Figure 5, a comprehensive driving cycle which is originated from the six typical driving cycles shown in Table 1 is employed as the test driving cycle, 18 namely, US06_HWY + MANHATTAN + WVUSUB + HWFET + CSHVR + NYCC. The I-LVQ and LVQ algorithm 21 are used to train the DCR model to verify the effectiveness of the model training algorithm. Each 200-s time length of the comprehensive driving cycle is considered the recognition target.…”
Section: Typementioning
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“…As shown in Figure 5, a comprehensive driving cycle which is originated from the six typical driving cycles shown in Table 1 is employed as the test driving cycle, 18 namely, US06_HWY + MANHATTAN + WVUSUB + HWFET + CSHVR + NYCC. The I-LVQ and LVQ algorithm 21 are used to train the DCR model to verify the effectiveness of the model training algorithm. Each 200-s time length of the comprehensive driving cycle is considered the recognition target.…”
Section: Typementioning
“…The LVQ neural network is a class of algorithms for pattern recognition and has been widely used in DCR. 21 However, the LVQ neural network cannot easily obtain ideal recognition accuracy and low computational complexity when the sample space is relatively large. 24 Therefore, an I-LVQ neural network is considered and applied to recognize driving cycle online.…”
Section: Dcr Model Based On the I-lvq Neural Networkmentioning
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