Structural rule-based modeling with granular computing
Structural rule-based modeling with granular computing
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DOI：
10.1016/j.asoc.2022.109519
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Tinghui Ouyang
中科院分区：
文献类型：
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作者：
Tinghui Ouyang
为了分析大数据时代复杂系统的动态行为，提出了一种新的基于规则的建模方法。该方法将结构信息挖掘和粒度计算结合起来，利用粒度模糊区间来反映系统在不确定性下的行为。主要贡献可以概括为三点。首先，利用DBSCAN在聚类任意形状的数据方面的优势，DBSCAN被应用于提取复杂系统中的结构信息作为规则的基础。其次，利用GrC进行规则形成和有效的基于规则的建模，例如，在输入空间中构造颗粒以细化结构DBSCAN簇，在输出空间中构造颗粒区间以反映系统的动态行为。第三，实验分析的基础上，三个不同的设计方案进行了研究，合成数据和公开可用的数据集。通过对比讨论，该方法可以优于传统的基于规则的模型，特别是有一个改进的比率为0.18%至1.48%的建议的综合指标。因此，可以得出结论，所提出的方法具有可行性和优势，反映了系统动态分析的结构性和不确定性的特点。
In order to analyze the dynamic behaviors of complex systems in the era of big data, a new rule-based modeling approach is proposed in this paper. This approach considers structural information mining and granular computing (GrC) in rule-based modeling, it also aims at utilizing granular fuzzy intervals to reflect systems’ behaviors on uncertainty. Major contributions can be summarized as three points. First, using DBSCAN’s advantages in clustering arbitrarily-shaped data, DBSCAN is applied to extract structural information as the basis of rules in complex systems. Second, GrC is leveraged for rule formation and effective rule-based modeling, e.g. granules constructed in input space to refine structural DBSCAN clusters, granular intervals constructed in output space with the principle of justifiable granulating to reflect system dynamic behaviors. Third, experimental analysis based on three different design scenarios was studied on both synthetic data and publicly available datasets. Through comparative discussion, the proposed approach can outperform the conventional rule-based models, specifically having an improvement ratio of 0.18% to 1.48% on the proposed comprehensive indicator. Therefore, it can be concluded that the proposed approach has the feasibility and advantages of reflecting the structural and uncertainty characteristics of system dynamic analysis.