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Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering

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Abstract

Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management.

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Acknowledgements

We greatly appreciate the editors and the reviewers for their constructive comments. We are grateful to Prof. Zhao Wanlei for his support in applying the BE algorithm “Deng CH, Zhao WL (2018) Clustering via boundary erosion. https://arxiv.org/abs/1804.04312”. This work was supported by the Natural Science Foundation of China under Grant 61773352 and Grant 62006033.

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Authors and Affiliations

  1. School of Science, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Fang Zhao & Lidong Wang

  2. School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Hongyue Guo

Authors
  1. Fang Zhao
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  2. Hongyue Guo
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  3. Lidong Wang
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Contributions

Fang Zhao contributed to writing—original draft and model visualization; Hongyue Guo contributed to methodology and supervision; Lidong Wang contributed to writing—review and supervision.

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Correspondence to Lidong Wang.

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Zhao, F., Guo, H. & Wang, L. Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering. Soft Comput 25, 9013–9023 (2021). https://doi.org/10.1007/s00500-021-05828-9

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