<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved combination optimization algorithm of principal component analysis and weighted K-means clustering is proposed. The algorithm introduces the maximum and minimum distance, weighted Euclidean distance, starting from the mean sum of the distances of the remaining clustering points, avoiding the influence of outliers and edge data. The proportion method is used to improve the principal component, and the characteristic influence factor obtained is used as the initial characteristic weight to construct a weighted Euclidean distance metric. According to the influence factors of feature contribution rate on clustering, a clustering method of feature weight influence factors is proposed. The representative feature factors are selected to highlight the clustering effect. Finally, the driving cycle of automobile is synthesized and the instantaneous fuel consumption is analyzed. The results show that: the difference value of speed acceleration joint distribution of the proposed method is only 1.05%, which saves 44.2% of the time compared with the traditional K-means clustering, and the driving cycle fitting degree is high, which can reflect the actual vehicle operation characteristics and fuel consumption.</p>
</abstract>

In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved combination optimization algorithm of principal component analysis and weighted K-means clustering is proposed. The algorithm introduces the maximum and minimum distance, weighted Euclidean distance, starting from the mean sum of the distances of the remaining clustering points, avoiding the influence of outliers and edge data. The proportion method is used to improve the principal component, and the characteristic influence factor obtained is used as the initial characteristic weight to construct a weighted Euclidean distance metric. According to the influence factors of feature contribution rate on clustering, a clustering method of feature weight influence factors is proposed. The representative feature factors are selected to highlight the clustering effect. Finally, the driving cycle of automobile is synthesized and the instantaneous fuel consumption is analyzed. The results show that: the difference value of speed acceleration joint distribution of the proposed method is only 1.05%, which saves 44.2% of the time compared with the traditional K-means clustering, and the driving cycle fitting degree is high, which can reflect the actual vehicle operation characteristics and fuel consumption.

Research on Driving Conditions and Fuel Consumption of Improved K-means Clustering Algorithm

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved...

Research on Driving Conditions and Fuel Consumption of Improved K-means Clustering Algorithm

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 1 - 10

DOI: https://doi.org/10.2478/ijanmc-2022-0011

Schlüsselwörter
Driving Cycle, Influence Factors, Feature Weight, Weighted K-Means Clustering

© 2022 Shuping Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research on Driving Conditions and Fuel Consumption of Improved K-means Clustering Algorithm

Online veröffentlicht: 26. Mai 2023

Seitenbereich: 1 - 10

DOI: https://doi.org/10.2478/ijanmc-2022-0011

SchlüsselwörterDriving Cycle, Influence Factors, Feature Weight, Weighted K-Means Clustering

© 2022 Shuping Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Driving Cycle, Influence Factors, Feature Weight, Weighted K-Means Clustering