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Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering
Soft Computing ( IF 3.050 ) Pub Date : 2021-05-04 , DOI: 10.1007/s00500-021-05828-9
Fang Zhao, Hongyue Guo, Lidong Wang

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.



中文翻译:

基于合理粒度和边界腐蚀聚类原理的基于规则的细粒度建模

由“ IF-THEN”模糊规则构建的基于规则的模型通常用于复杂的非线性系统中。在这项研究中,建立了一种新的建模方法来基于实验证据生成模糊规则。这种建模是通过利用边界侵蚀算法对输入样本进行聚类,并通过合理粒度的原则对相应的输出进行粒化来实现的。为了进一步检查设计的基于规则的模型在不同粒度级别下的性能,设计了一个具有更精细信息粒度的模型,用于在每个群集中提取规则。在合成和舰船数据集上对提出的模型进行了评估,其中基于信息粒子的收敛性和特异性,将粒度输出与原始数据值之间的比较视为评估指标。

更新日期:2021-05-04
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