中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief

文献类型：期刊论文

作者Shi, Zhenhua1; Wu, Dongrui1; Guo, Chenfeng1; Zhao, Changming1; Cui, Yuqi1; Wang, Fei-Yue2
刊名INFORMATION SCIENCES
出版日期2021-10-01
卷号574页码:490-504
ISSN号0020-0255
关键词TSK fuzzy system Mini-batch gradient descent DropRule Powerball AdaBelief Fuzzy c-means clustering
DOI10.1016/j.ins.2021.05.084
通讯作者Wu, Dongrui(drwu@hust.edu.cn) ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. (c) 2021 Elsevier Inc. All rights reserved.
WOS关键词GRADIENT DESCENT ; NEURAL-NETWORKS ; SYSTEMS
资助项目Technology Innovation Project of Hubei Province of China[2019AEA171] ; National Natural Science Foundation of China[61873321] ; National Natural Science Foundation of China[U1913207] ; International Science and Technology Cooperation Program of China[2017YFE0128300]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000691237400004
资助机构Technology Innovation Project of Hubei Province of China ; National Natural Science Foundation of China ; International Science and Technology Cooperation Program of China
源URL[http://ir.ia.ac.cn/handle/173211/45899]  
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Wu, Dongrui; Wang, Fei-Yue
作者单位1.Huazhong Univ Sci & Technol, Key Lab, Sch Artificial Intelligence & Automat, Minist Educ Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Zhenhua,Wu, Dongrui,Guo, Chenfeng,et al. FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief[J]. INFORMATION SCIENCES,2021,574:490-504.
APA Shi, Zhenhua,Wu, Dongrui,Guo, Chenfeng,Zhao, Changming,Cui, Yuqi,&Wang, Fei-Yue.(2021).FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief.INFORMATION SCIENCES,574,490-504.
MLA Shi, Zhenhua,et al."FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, Droprule, and Powerball Adabelief".INFORMATION SCIENCES 574(2021):490-504.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。