Development of Granular Fuzzy Relation Equations Based on a Subset of Data

doi:10.1109/JAS.2021.1004054

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	Development of Granular Fuzzy Relation Equations Based on a Subset of Data
	Dan Wang; Xiubin Zhu; Witold Pedycz; Zhenhua Yu; Zhiwu Li
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:8 页码:1416-1427
摘要	Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.
关键词	A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization (PSO)
DOI	10.1109/JAS.2021.1004054
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44593
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Dan Wang,Xiubin Zhu,Witold Pedycz,et al. Development of Granular Fuzzy Relation Equations Based on a Subset of Data[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(8):1416-1427.
APA	Dan Wang,Xiubin Zhu,Witold Pedycz,Zhenhua Yu,&Zhiwu Li.(2021).Development of Granular Fuzzy Relation Equations Based on a Subset of Data.IEEE/CAA Journal of Automatica Sinica,8(8),1416-1427.
MLA	Dan Wang,et al."Development of Granular Fuzzy Relation Equations Based on a Subset of Data".IEEE/CAA Journal of Automatica Sinica 8.8(2021):1416-1427.

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