Journal of Northeastern University Natural Science ›› 2016, Vol. 37 ›› Issue (3): 387-391.DOI: 10.12068/j.issn.1005-3026.2016.03.018

• Mechanical Engineering • Previous Articles     Next Articles

Sample Classification Method for Green Process Evaluation Based on Fuzzy Clustering

WANG Yu-gang1, XIU Shi-chao1, WANG Ke-yuan2   

  1. 1.School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2.Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
  • Received:2015-01-07 Revised:2015-01-07 Online:2016-03-15 Published:2016-03-07
  • Contact: WANG Yu-gang
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Abstract: Due to the uncertainty, multidimensionality and significant difference of the evaluation samples of green process, a novel algorithm of kernel-based fuzzy possibilistic clustering was proposed in order to achieve reasonable sample classification. Kernel fuzzy clustering, possibilistic clustering and subtraction clustering were combined to improve the accuracy of clustering and cluster validity index was used as the classification condition to obtain the optimal classification number. The simulation results showed that this algorithm has good validity and robustness. When the algorithm is applied to classify the evaluation samples of green process, good classification effects are gained, which verifies its practicability.

Key words: kernel fuzzy clustering, possibilistic clustering, subtraction clustering, validity index, green process, sample classification

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