Development of Granular Fuzzy Relation Equations Based on a Subset of Data
文献类型：期刊论文
| 作者 | Dan Wang ; Xiubin Zhu; Witold Pedycz; Zhenhua Yu; Zhiwu Li
|
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2021 |
| 卷号 | 8期号:8页码:1416-1427 |
| 关键词 | A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization (PSO) |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2021.1004054 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44593]  |
| 专题 | 自动化研究所_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. |
入库方式： OAI收割
来源：自动化研究所
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