A fast learning method for data-driven design of interval type-2 fuzzy logic system

doi:10.3233/JIFS-16799

CORC > 自动化研究所 > 中国科学院自动化研究所 > 综合信息系统研究中心

	A fast learning method for data-driven design of interval type-2 fuzzy logic system
	Li, Chengdong 1; Zhang, Guiqing 1; Yi, Jianqiang2 ; Shang, Fang 1; Gao, Junlong2
刊名	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
	2017
卷号	32 期号:3 页码:2705-2715
关键词	Data-driven Method Fast Learning Fuzzy Logic System Anfis Type-2 Fuzzy
DOI	10.3233/JIFS-16799
文献子类	Article
英文摘要	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.
WOS关键词	INFERENCE SYSTEM ; NEURAL-NETWORKS ; OPTIMIZATION ; PREDICTION ; ALGORITHM ; IDENTIFICATION ; UNCERTAINTY ; FOOTPRINT ; ANFIS
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000395904400085
资助机构	National Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities(ZR2015JL021) ; 61273149 ; 61573225)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/14405]
专题	自动化研究所_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.