DBSCAN-based granular descriptors for rule-based modeling
DBSCAN-based granular descriptors for rule-based modeling
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DOI：
10.1007/s00500-022-07514-w
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发表时间：
2022-09
期刊：
影响因子：
4.1
通讯作者：
Tinghui Ouyang;Xinhui Zhang
中科院分区：
文献类型：
--
作者：
Tinghui Ouyang;Xinhui Zhang
基于规则的建模是复杂非线性和非数值系统建模的一种有用方法，例如具有语言信息。然而，大数据时代复杂系统的建模对传统的基于规则的建模提出了新的挑战，如计算量大、规则的表示能力低等。为了解决这些问题，本文提出了一种先进的基于规则的建模方法——基于dbscan的粒度描述符。首先，为了理解数据的本质特征，增强规则的表示能力，采用DBSCAN聚类算法获得数据结构，该算法在应对不同几何形状时具有很高的灵活性。其次，在数据结构的精细化表示中构造了大量的粒度描述符，并用于模糊规则的形成。这种粒度计算过程可以有效降低大数据分析的计算开销。最后，提出的基于规则的模型由模糊规则和区间输出组成，它们分别由结构颗粒描述符和合理粒化产生。对合成数据和公开数据的实验研究表明，该方法在建模和耗时方面都优于传统的基于规则的FCM建模。验证了该方法在实际复杂工程系统建模中的可行性和实用性。
Rule-based modeling is a useful approach in modeling both complex nonlinear and non-numeric systems, e.g. having linguistic information. However, modeling complex systems in big data era brings new challenges for conventional rule-based modeling, such as high computation overhead and low representation ability of rule. To address these problems, this paper proposed an advanced rule-based modeling method-based DBSCAN-inspired granular descriptors. First, to understand the essential characteristics of data and enhance rules’ representation ability, data structures are obtained by DBSCAN clustering algorithm, which has high flexibility at coping with diverse geometry. Second, numerous granular descriptors are constructed in the refined representation of data structures and used for fuzzy rule formation. This granular computing process could effectively reduce computation overhead of big data analysis. Finally, the proposed rule-based model consists of fuzzy rules and interval outputs, which are resulted from structural granular descriptors and justifiable granulating respectively. Experimental studies concerning synthetic data and publicly available data illustrated that the proposed method can achieve prior performance on both modeling and time consuming than conventional rule-based modeling via FCM. Therefore, it is verified the developed approach is feasible and useful to be applied in modeling real complex engineering systems.