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Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems
Li, Chengdong1; Gao, Junlong2; Yi, Jianqiang2; Zhang, Guiqing1
2018-02-01
发表期刊IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷号26期号:1页码:56-71
文章类型Article
摘要The single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently solve the fuzzy rule explosion phenomenon, which usually occurs in the multivariable modeling and/or control applications. However, the performance of the SIRMs connected fuzzy inference system (SIRM-FIS) is limited due to its simple input-output mapping. In this paper, to further enhance the performance of SIRM-FIS, a functionally weighted SIRM-FIS (FWSIRM-FIS), which adopts multivariable functional weights to measure the important degrees of the SIRMs, is presented. Then, in order to show the fundamental differences of the SIRMs methods, properties of the traditional SIRM-FIS, the type-2 SIRM-FIS (T2SIRM-FIS), the functional SIRM-FIS (FSIRM-FIS), the SIRMs model with single-variable functional weights (SIRM-FW), and FWSIRM-FIS are explored. These properties demonstrate that the proposed FWSIRM-FIS has more general and complex input-output mapping than the existing SIRMs methods. Such properties theoretically guarantee that better performance can be achieved by FWSIRM-FIS. Furthermore, based on the least-squares method, a novel data-driven optimization method is presented for the parameter learning of FWSIRM-FIS. It can also be used to optimize the parameters of SIRM-FIS, T2SIRM-FIS, FSIRM-FIS, and SIRM-FW. Due to the properties of the least-squares method, the proposed parameter learning algorithm can overcome the drawbacks of the gradients-based parameter learning methods and obtain both smallest training errors and smallest parameters. Finally, to show the effectiveness and superiority of FWSIRM-FIS and the proposed optimization method, six examples and detailed comparisons are given. Simulation results show that FWSIRM-FIS can obtain better performance than the other SIRMs methods, and, compared with some well-known methods, FWSIRM-FIS can achieve similar or better performance but has much less parameters and faster training speed.
关键词Data-driven Method Fuzzy Inference System (Fis) Least-squares Method Parameter Learning Single-input Rule Module (Sirm)
WOS标题词Science & Technology ; Technology
DOI10.1109/TFUZZ.2016.2637369
关键词[WOS]INVERTED PENDULUM SYSTEM ; NEURAL-NETWORKS ; IDENTIFICATION ; MODEL ; OPTIMIZATION ; PREDICTION ; STABILIZATION ; MONOTONICITY ; CONTROLLER ; ANFIS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province(ZR2015JL021) ; 61421004 ; 61573225 ; 61374187 ; 61105077)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000424985400006
引用统计
被引频次:17[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/173211/21954
专题综合信息系统研究中心
作者单位1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Li, Chengdong,Gao, Junlong,Yi, Jianqiang,et al. Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2018,26(1):56-71.
APA Li, Chengdong,Gao, Junlong,Yi, Jianqiang,&Zhang, Guiqing.(2018).Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems.IEEE TRANSACTIONS ON FUZZY SYSTEMS,26(1),56-71.
MLA Li, Chengdong,et al."Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems".IEEE TRANSACTIONS ON FUZZY SYSTEMS 26.1(2018):56-71.
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