Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence

doi:10.3390/app11167766

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence
	Chen, Dewang 1,2,3; Cai, Jijie 1,3; Huang, Yunhu 1,3; Lv, Yisheng 4
刊名	APPLIED SCIENCES-BASEL
	2021-08-01
卷号	11 期号:16 页码:19
关键词	interpretability high-dimensional data adaptive neuro-fuzzy inference system deep neural fuzzy system
DOI	10.3390/app11167766
通讯作者	Huang, Yunhu(N190310001@fzu.edu.cn)
英文摘要	Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability.
资助项目	National Natural Science Foundation of China[61976055] ; special fund for education and scientific research of Fujian Provincial Department of Finance[GY-Z21001] ; open project of State Key Laboratory of Management and Control for Complex Systems[20210116]
WOS关键词	MINIBATCH GRADIENT DESCENT ; ANFIS ; CLASSIFICATION ; REGULARIZATION
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000688605800001
资助机构	National Natural Science Foundation of China ; special fund for education and scientific research of Fujian Provincial Department of Finance ; open project of State Key Laboratory of Management and Control for Complex Systems
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45905]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Huang, Yunhu
作者单位	1.Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China 2.Fujian Univ Technol, Sch Transportat, Fuzhou 350108, Peoples R China 3.Fuzhou Univ, Key Lab Intelligent Metro Univ Fujian Prov, Fuzhou 350108, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Dewang,Cai, Jijie,Huang, Yunhu,et al. Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence[J]. APPLIED SCIENCES-BASEL,2021,11(16):19.
APA	Chen, Dewang,Cai, Jijie,Huang, Yunhu,&Lv, Yisheng.(2021).Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence.APPLIED SCIENCES-BASEL,11(16),19.
MLA	Chen, Dewang,et al."Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence".APPLIED SCIENCES-BASEL 11.16(2021):19.