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Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

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Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises. / Yang, Zhao Xu; Rong, Hai-Jun; Angelov, Plamen et al.

In: IEEE Transactions on Fuzzy Systems, Vol. 30, No. 7, 31.07.2022, p. 2649 - 2664.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Yang, ZX, Rong, H-J, Angelov, P & Yang, ZX 2022, 'Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2649 - 2664. https://doi.org/10.1109/TFUZZ.2021.3090898

APA

Yang, Z. X., Rong, H-J., Angelov, P., & Yang, Z. X. (2022). Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises. IEEE Transactions on Fuzzy Systems, 30(7), 2649 - 2664. https://doi.org/10.1109/TFUZZ.2021.3090898

Vancouver

Yang ZX, Rong H-J, Angelov P, Yang ZX. Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises. IEEE Transactions on Fuzzy Systems. 2022 Jul 31;30(7): 2649 - 2664. Epub 2021 Jun 22. doi: 10.1109/TFUZZ.2021.3090898

Author

Yang, Zhao Xu ; Rong, Hai-Jun ; Angelov, Plamen et al. / Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises. In: IEEE Transactions on Fuzzy Systems. 2022 ; Vol. 30, No. 7. pp. 2649 - 2664.

Bibtex

@article{ead7b2052c5944fa8ef2c88c059bc006,
title = "Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises",
abstract = "Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this paper, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our work, an adaptive maximum correntropy extend Kalman filter (AMCEKF) is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise-free and noisy conditions.",
keywords = "evolving fuzzy behaviours, Kalman filter, maximum correntropy, model sufficiency",
author = "Yang, {Zhao Xu} and Hai-Jun Rong and Plamen Angelov and Yang, {Zhi Xin}",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2022",
month = jul,
day = "31",
doi = "10.1109/TFUZZ.2021.3090898",
language = "English",
volume = "30",
pages = " 2649 -- 2664",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "7",

}

RIS

TY - JOUR

T1 - Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises

AU - Yang, Zhao Xu

AU - Rong, Hai-Jun

AU - Angelov, Plamen

AU - Yang, Zhi Xin

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/7/31

Y1 - 2022/7/31

N2 - Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this paper, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our work, an adaptive maximum correntropy extend Kalman filter (AMCEKF) is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise-free and noisy conditions.

AB - Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this paper, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our work, an adaptive maximum correntropy extend Kalman filter (AMCEKF) is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise-free and noisy conditions.

KW - evolving fuzzy behaviours

KW - Kalman filter

KW - maximum correntropy

KW - model sufficiency

U2 - 10.1109/TFUZZ.2021.3090898

DO - 10.1109/TFUZZ.2021.3090898

M3 - Journal article

VL - 30

SP - 2649

EP - 2664

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

IS - 7

ER -