Application of Fuzzy C-Mean Clustering Based on Multi-polar Fuzzy Entropy Improvement in Dynamic truck Scale Cheating Recognition

EasyChair Preprint 2939

Abstract

In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-pole fuzzy feature information. Additionally, the traditional classification method leads to slow speed and inaccuracy because of its difficulties. Therefore, by considering a multi-polar fuzzy feature classification of defects, a fuzzy c-means (FCM) clustering algorithm based on multi-pole fuzzy entropy is proposed. Firstly, according to the evaluation of available characteristics, the characteristic value of clustering samples is established. Secondly, we calculated the multi-polar fuzzy entropy of clustering samples. Finally, an improved FCM clustering algorithm based on multi-polar fuzzy entropy is presented. The experimental results of the data set collected from 5 different types of weighing cheating cars demonstrate that the algorithm improves the classification accuracy of FCM with multi-polar fuzzy feature information clustering and reduces significantly both the number of iterations and the classification time.

Keyphrases: Multi-polar fuzzy entropy, dynamic truck scale, fuzzy c-means clustering, multi-polar fuzzy feature

@booklet{EasyChair:2939,
  author    = {Zhenyu Lu and Xianyun Huang},
  title     = {Application of Fuzzy C-Mean Clustering Based on Multi-polar Fuzzy Entropy Improvement in Dynamic truck Scale Cheating Recognition},
  howpublished = {EasyChair Preprint 2939},
  year      = {EasyChair, 2020}}