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A Fuzzy Identification Method Based on the Likelihood Function and Noise Clustering Algorithm

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Abstract

In this paper, based on the modified fuzzy c-regression model and noise clustering algorithm, a fuzzy identification method is proposed. Firstly, by considering the relations for the real model, the established model, and the outliers, a modified objective function with noise is proposed to alleviate the affection of noise. Additionally, the consequent parameters of the fuzzy model can be obtained by the iterative formula which obtained by the Lagrangian formula. Furthermore, a modified membership function, which is involved the likelihood function, is propounded to get a more suitable multivariate normal distribution for the data points. Lastly, two examples are illustrated to show the validity and effectiveness of the proposed results.

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Acknowledgements

This research was funded by Ministry of Science and Technology, Taiwan, under grants MOST-110-2221-E-110-067 and MOST-111-2221-E-110-061.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Sun Yat-sen University, Lien-hai Rd., Kaohsiung, 804, Taiwan

    Shun-Hung Tsai

  2. Graduate Institute of Automation Technology, National Taipei University of Technology, Zhongxiao E. Rd., Taipei, 106, Taiwan

    Shun-Hung Tsai & Yi-Ting Chen

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  1. Shun-Hung Tsai
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  2. Yi-Ting Chen
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Correspondence to Shun-Hung Tsai.

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Tsai, SH., Chen, YT. A Fuzzy Identification Method Based on the Likelihood Function and Noise Clustering Algorithm. Int. J. Fuzzy Syst. 25, 136–144 (2023). https://doi.org/10.1007/s40815-022-01366-0

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  • DOI: https://doi.org/10.1007/s40815-022-01366-0

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