International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 1590 - 1597

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

Authors
Zhenyu Lu1, Xianyun Huang2, *
1Artificial Intelligence Institute, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
2Scientific Research Post, Suzhou Institute of Metrology, Suzhou, Jiangsu, 215128, China
*Corresponding author. Email: huangxianyun06@163.com
Received 31 May 2020, Accepted 15 September 2020, Available Online 1 October 2020.
DOI
10.2991/ijcis.d.200923.001How to use a DOI?
Keywords
Multi-polar fuzzy entropy; Fuzzy C-means clustering; Multi-polar fuzzy feature; Dynamic truck scale
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-polar 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-polar 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.

Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
1590 - 1597
Publication Date
2020/10/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200923.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Zhenyu Lu
AU  - Xianyun Huang
PY  - 2020
DA  - 2020/10/01
TI  - Application of Fuzzy C-Mean Clustering Based on Multi-Polar Fuzzy Entropy Improvement in Dynamic Truck Scale Cheating Recognition
JO  - International Journal of Computational Intelligence Systems
SP  - 1590
EP  - 1597
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200923.001
DO  - 10.2991/ijcis.d.200923.001
ID  - Lu2020
ER  -