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A fast learning method for data-driven design of interval type-2 fuzzy logic system
Li, Chengdong1; Zhang, Guiqing1; Yi, Jianqiang2; Shang, Fang1; Gao, Junlong2
Source PublicationJOURNAL OF INTELLIGENT & FUZZY SYSTEMS
2017
Volume32Issue:3Pages:2705-2715
SubtypeArticle
AbstractIn this study, we propose a novel fast learning data-driven method for the design of interval type-2 fuzzy logic system (IT2FLS). In order to accelerate the learning speed, we present a parallel mechanism to generate the antecedents of the IT2FLS and the least square method based learning algorithm to optimize the consequents. Firstly, driven by different sub-data sets, corresponding type-1 fuzzy logic systems (T1FLSs) which have the same initial fuzzy partition (thus the same initial fuzzy rule base) are parallelly obtained through the popular ANFIS method. Then, an ensembling strategy is proposed to form the type-2 fuzzy partition for each input variable through merging corresponding type-1 fuzzy sets (T1FSs) in the type-1 fuzzy partitions of the learned T1FLSs. By this strategy, the antecedents of the IT2FLS are determined and then fixed, however, the consequent parameters still need to be optimized. To achieve both excellent performance and fast training speed, a least square method based learning algorithm is provided for the optimization of the consequent parameters. Finally, three benchmark problems and one real-world application are given, and detailed comparisons with some well performed methods are made. Simulation and comparison results have verified the effectiveness and superiorities of the proposed method.
KeywordData-driven Method Fast Learning Fuzzy Logic System Anfis Type-2 Fuzzy
WOS HeadingsScience & Technology ; Technology
DOI10.3233/JIFS-16799
WOS KeywordINFERENCE SYSTEM ; NEURAL-NETWORKS ; OPTIMIZATION ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; UNCERTAINTY ; FOOTPRINT ; ANFIS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities(ZR2015JL021) ; 61273149 ; 61573225)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000395904400085
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14405
Collection综合信息系统研究中心
Affiliation1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Li, Chengdong,Zhang, Guiqing,Yi, Jianqiang,et al. A fast learning method for data-driven design of interval type-2 fuzzy logic system[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2017,32(3):2705-2715.
APA Li, Chengdong,Zhang, Guiqing,Yi, Jianqiang,Shang, Fang,&Gao, Junlong.(2017).A fast learning method for data-driven design of interval type-2 fuzzy logic system.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,32(3),2705-2715.
MLA Li, Chengdong,et al."A fast learning method for data-driven design of interval type-2 fuzzy logic system".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 32.3(2017):2705-2715.
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