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中南大学学报(英文版)

Journal of Central South University

Vol. 22    No. 7    July 2015

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Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm
CHU Fei(褚菲)1, MA Xiao-ping(马小平)1, WANG Fu-li(王福利) 2, JIA Run-da(贾润达) 2

1. Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal mining,
China University of Mining and Technology, Xuzhou 221116, China;
2. State Key Laboratory of Integrated Automation for Process Industries,
Northeastern University, Shenyang 110819, China

Abstract:A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator (partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares (PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.

 

Key words: mamdani-type fuzzy system; robust system; subtractive clustering algorithm; outlier; partial robust M-regression

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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