Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification

doi:10.1109/TCYB.2018.2857815

	Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
	Feng, Shuangg 3,4; Chen, C. L. Philip 1,2,3
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2020-02-01
卷号	50 期号:2 页码:414-424
关键词	Broad learning system (BLS) classification k-means regression Takagi-Sugeno (TS) fuzzy system
ISSN号	2168-2267
DOI	10.1109/TCYB.2018.2857815
通讯作者	Chen, C. L. Philip(philip.chen@ieee.org)
英文摘要	A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The k-means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neuro-fuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent.
资助项目	National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61751205] ; National Natural Science Foundation of China[61572540] ; Macau Science and Technology Development Fund (FDCT)[019/2015/A1] ; Macau Science and Technology Development Fund (FDCT)[079/2017/A2] ; Macau Science and Technology Development Fund (FDCT)[024/2015/AMJ] ; University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai
WOS关键词	RESTRICTED BOLTZMANN MACHINE ; INFERENCE SYSTEM ; NETWORKS ; RULE ; IDENTIFICATION ; APPROXIMATION ; ALGORITHM ; EQUIVALENCE ; CONTROLLER
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000506849800002
资助机构	National Natural Science Foundation of China ; Macau Science and Technology Development Fund (FDCT) ; University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29513]
专题	离退休人员
通讯作者	Chen, C. L. Philip
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China 2.Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China 3.Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China 4.Beijing Normal Univ, Sch Appl Math, Zhuhai Campus, Zhuhai 519085, Peoples R China
推荐引用方式 GB/T 7714	Feng, Shuangg,Chen, C. L. Philip. Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(2):414-424.
APA	Feng, Shuangg,&Chen, C. L. Philip.(2020).Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification.IEEE TRANSACTIONS ON CYBERNETICS,50(2),414-424.
MLA	Feng, Shuangg,et al."Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification".IEEE TRANSACTIONS ON CYBERNETICS 50.2(2020):414-424.