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 multipolar 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.
Highlights
Artificial intelligence being rich in information, has been widely used in various research fields to solve the complex problems through computer simulation
Cayley bipolar fuzzy graphs theory is proposed by Alshehri et al [1] to solve the real time system modeling problem, where the level of information inherent in the system varies with the different levels of precision, In 2014, Mesiarová et al [2] expanded bipolar fuzzy sets to m-polar fuzzy sets and promoted the development of bipolar fuzzy theory
In 2016, Zhou et al [3] discussed the problem of nonlinear optimization with bipolar fuzzy relation equation constraints and promoted the development of bipolar fuzzy theory
Summary
Artificial intelligence being rich in information, has been widely used in various research fields to solve the complex problems through computer simulation. Cayley bipolar fuzzy graphs theory is proposed by Alshehri et al [1] to solve the real time system modeling problem, where the level of information inherent in the system varies with the different levels of precision, In 2014, Mesiarová et al [2] expanded bipolar fuzzy sets to m-polar fuzzy sets and promoted the development of bipolar fuzzy theory. FCM is a fuzzy clustering algorithm based on an objective function proposed by Dunn and promoted by Bezdek [4]. It has been widely used in the image segmentation, automatic speech recognition, fault diagnosis and customer classification. In the traditional fuzzy C-clustering algorithm, the eigenvalues of clustering objects are
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