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Type-2 Fuzzy Broad Learning System
Han, Honggui1; Liu, Zheng1; Liu, Hongxu1; Qiao, Junfei1; Chen, C. L.Philip2,3
2022-10-01
Source PublicationIEEE Transactions on Cybernetics
ABS Journal Level3
ISSN2168-2267
Volume52Issue:10Pages:10352-10363
Abstract

The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a group of interval type-2 fuzzy neurons was used to replace the feature neurons of BLS. Then, the representation of BLS can be improved to obtain good robustness. Second, a fuzzy pseudoinverse learning algorithm was designed to adjust the parameter of type-2 FBLS. Then, the proposed type-2 FBLS was able to maintain the fast computational nature of BLS. Third, a theoretical analysis on the convergence of type-2 FBLS was given to show the computational efficiency. Finally, some benchmark and practical problems were used to test the merits of type-2 FBLS. The experimental results indicated that the proposed type-2 FBLS can achieve outstanding performance.

KeywordBroad Learning System (Bls) Fuzzy Pseudoinverse Learning (Fpl) Algorithm Interval Type-2 Fuzzy Neuron Robustness
DOI10.1109/TCYB.2021.3070578
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000732152800001
Scopus ID2-s2.0-85104671125
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Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intellige, Engn Res Ctr Digital Community,Minist Educ,Beijin, Beijing 100124, Peoples R China
2.University of Macau, Faculty of Science and Technology, SAR 99999, Macao
3.South China University of Technology, School of Computer Science and Engineering, Guangzhou, 510006, China
Recommended Citation
GB/T 7714
Han, Honggui,Liu, Zheng,Liu, Hongxu,et al. Type-2 Fuzzy Broad Learning System[J]. IEEE Transactions on Cybernetics,2022,52(10):10352-10363.
APA Han, Honggui,Liu, Zheng,Liu, Hongxu,Qiao, Junfei,&Chen, C. L.Philip.(2022).Type-2 Fuzzy Broad Learning System.IEEE Transactions on Cybernetics,52(10),10352-10363.
MLA Han, Honggui,et al."Type-2 Fuzzy Broad Learning System".IEEE Transactions on Cybernetics 52.10(2022):10352-10363.
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