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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
2017
发表期刊JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷号32期号:3页码:2705-2715
文章类型Article
摘要In 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.
关键词Data-driven Method Fast Learning Fuzzy Logic System Anfis Type-2 Fuzzy
WOS标题词Science & Technology ; Technology
DOI10.3233/JIFS-16799
关键词[WOS]INFERENCE SYSTEM ; NEURAL-NETWORKS ; OPTIMIZATION ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; UNCERTAINTY ; FOOTPRINT ; 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 for Outstanding Young Talents in Provincial Universities(ZR2015JL021) ; 61273149 ; 61573225)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000395904400085
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14405
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, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式
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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