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Correntropy-Based Evolving Fuzzy Neural System
Bao, Rong-Jing1; Rong, Hai-Jun1; Angelov, Plamen P.2; Chen, Badong3; Wong, Pak Kin4
2018-06
Source PublicationIEEE TRANSACTIONS ON FUZZY SYSTEMS
ISSN1063-6706
Volume26Issue:3Pages:1324-1338
Abstract

In this paper, a correntropy-based evolving fuzzy neural system (CEFNS) is proposed for approximation of nonlinear systems. Different from the commonly usedmean-square error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build evolving fuzzy neural system (EFNS) based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used mean-square error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base, and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on themaximum correntropy criterion, where the correntropy is used as the cost function so as to improve the non-Gaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: Gaussian noise; and non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other EFNSs, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

KeywordCorrentropy Evolving Fuzzy Neural System (Efns) Mean-square Error (Mse) Nonlinear System Steady-state Excess Mean Square Error (Emse)
DOI10.1109/TFUZZ.2017.2719619
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000433957900019
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Affiliation1.State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, China
2.Data Science Group, School of Computing and Communications, Lancaster University, Lancaster LA1 4WA, U.K.
3.Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
4.Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau
Recommended Citation
GB/T 7714
Bao, Rong-Jing,Rong, Hai-Jun,Angelov, Plamen P.,et al. Correntropy-Based Evolving Fuzzy Neural System[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2018,26(3):1324-1338.
APA Bao, Rong-Jing,Rong, Hai-Jun,Angelov, Plamen P.,Chen, Badong,&Wong, Pak Kin.(2018).Correntropy-Based Evolving Fuzzy Neural System.IEEE TRANSACTIONS ON FUZZY SYSTEMS,26(3),1324-1338.
MLA Bao, Rong-Jing,et al."Correntropy-Based Evolving Fuzzy Neural System".IEEE TRANSACTIONS ON FUZZY SYSTEMS 26.3(2018):1324-1338.
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