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Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm

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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.

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Authors and Affiliations

  1. Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining, China University of Mining and Technology, Xuzhou, 221116, China

    Fei Chu  (褚菲) & Xiao-ping Ma  (马小平)

  2. State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Fu-li Wang  (王福利) & Run-da Jia  (贾润达)

Authors
  1. Fei Chu  (褚菲)
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  2. Xiao-ping Ma  (马小平)
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  3. Fu-li Wang  (王福利)
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  4. Run-da Jia  (贾润达)
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Corresponding author

Correspondence to Fei Chu  (褚菲).

Additional information

Foundation item: Project(61473298) supported by the National Natural Science Foundation of China; Project(2015QNA65) supported by Fundamental Research Funds for the Central Universities, China

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Chu, F., Ma, Xp., Wang, Fl. et al. Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm. J. Cent. South Univ. 22, 2620–2628 (2015). https://doi.org/10.1007/s11771-015-2792-3

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  • DOI: https://doi.org/10.1007/s11771-015-2792-3

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