Slashing Quality Index Modeling and Simulation Based on Data Dispersion Clustering

Authors

Yuxian Zhang, 1. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China;;
Xiaoyi Qian, 1. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China;;
Dong Xiao, 1. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China;;
Jianhui Wang, 2. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;

Abstract

Abstract: For the sensitivity of noise and outliers data in the typical partitioning clustering algorithm, a clustering algorithm based on data dispersion was proposed. The data dispersion was defined and introduced to a non-Euclidean distance. The similarity metric was established, and the data clustering was realized. The optimal clustering number was obtained by the validity function based on improved partition coefficient. Then the proposed clustering algorithm was applied to quality index model in slashing process. A size add-on quality index model was built by radial basis function neural networks. The node number of hidden layer was determined and the center of radial basis function was obtained by the proposed clustering algorithm. The empirical result shows that the clustering result is insensitive to noise and outliers data, and the accuracy of size add-on quality index model is higher.

Recommended Citation

Zhang, Yuxian; Qian, Xiaoyi; Xiao, Dong; and Wang, Jianhui (2020) "Slashing Quality Index Modeling and Simulation Based on Data Dispersion Clustering," Journal of System Simulation: Vol. 28: Iss. 8, Article 1.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss8/1

First Page

1707

Revised Date

2015-11-26

DOI Link

https://doi.org/

Last Page

1714

CLC

TP183

Recommended Citation

Zhang Yuxian, Qian Xiaoyi, Dong Xiao, Wang Jianhui. Slashing Quality Index Modeling and Simulation Based on Data Dispersion Clustering[J]. Journal of System Simulation, 2016, 28(8): 1707-1714.