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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
AbstractThe 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.
KeywordData-driven Method Fuzzy Inference System (Fis) Least-squares Method Parameter Learning Single-input Rule Module (Sirm)
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province(ZR2015JL021) ; 61421004 ; 61573225 ; 61374187 ; 61105077)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000424985400006
Citation statistics
Cited Times:42[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
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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