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Fuzzy Rule-Based Models: A Design with Prototype Relocation and Granular Generalization
Information Sciences ( IF 8.1 ) Pub Date : 2021-01-26 , DOI: 10.1016/j.ins.2020.12.093
Yan Li , Chao Chen , Xingchen Hu , Jindong Qin , Yang Ma

Fuzzy rule-based models and the extension of classical fuzzy models have been widely used in many domains. From a holistic perspective, regardless of the design methods and rules adopted in a fuzzy model, the determination of fuzzy sets is a pivotal issue. In the proposed methods, instead of traditional data clustering with no directional tendency, we introduce an optimization algorithm that can adjust the position of the prototypes of zero- and first-order fuzzy models to learn internal structure information from the data in the process of parameter identification. Furthermore, to build a granular fuzzy model, the prototypes are then scaled to more robust intervals by generating information granularity with specific semantics such that they split the whole output space. Particle swarm optimization algorithm is applied to adjust both the locations of the prototypes and the allocation of information granularity to improve the performance of the data-driven models. Experimental studies on synthetic and real-world datasets are provided to demonstrate the effectiveness of these methods.



中文翻译:

基于模糊规则的模型:具有原型重定位和粒度泛化的设计

基于模糊规则的模型和经典模糊模型的扩展已广泛应用于许多领域。从整体的角度来看,无论模糊模型采用的设计方法和规则如何,确定模糊集都是一个关键问题。在提出的方法中,我们引入了一种优化算法,该算法可以调整零阶和一阶模糊模型原型的位置,以在参数处理过程中从数据中学习内部结构信息,而不是使用传统的没有方向性的数据聚类方法鉴别。此外,为了构建粒度模糊模型,然后通过生成具有特定语义的信息粒度将原型缩放到更健壮的间隔,从而将整个输出空间分割开。应用粒子群优化算法来调整原型的位置和信息粒度的分配,以改善数据驱动模型的性能。提供了对合成数据集和现实世界数据集的实验研究,以证明这些方法的有效性。

更新日期:2021-03-01
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