Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107280
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Wang, S | en_US |
dc.creator | Dang, L | en_US |
dc.creator | Wang, W | en_US |
dc.creator | Qian, G | en_US |
dc.creator | Tse, CK | en_US |
dc.date.accessioned | 2024-06-13T01:05:04Z | - |
dc.date.available | 2024-06-13T01:05:04Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/107280 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. | en_US |
dc.rights | The following publication S. Wang, L. Dang, W. Wang, G. Qian and C. K. Tse, "Kernel Adaptive Filters With Feedback Based on Maximum Correntropy," in IEEE Access, vol. 6, pp. 10540-10552, 2018 is available at https://doi.org/10.1109/ACCESS.2018.2808218. | en_US |
dc.subject | Convergence | en_US |
dc.subject | Feedback structure | en_US |
dc.subject | Kernel adaptive filters | en_US |
dc.subject | Maximum correntropy | en_US |
dc.subject | Minimum mean square error | en_US |
dc.title | Kernel adaptive filters with feedback based on maximum correntropy | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 10540 | en_US |
dc.identifier.epage | 10552 | en_US |
dc.identifier.volume | 6 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2018.2808218 | en_US |
dcterms.abstract | This paper presents novel kernel adaptive filters with feedback, namely, kernel recursive maximum correntropy with multiple feedback (KRMC-MF) and its simplified version, a linear recurrent kernel online learning algorithm based on maximum correntropy criterion (LRKOL-MCC). In LRKOL-MCC and KRMC-MF, single output and multiple outputs based on single delay are utilized to construct their feedback structure, respectively. Compared with the minimum mean square error criterion, the maximum correntropy criterion (MCC) adopted by LRKOL-MCC and KRMC-MF captures higher order statistics of errors. The proposed filters are, therefore, robust against outliers. Therefore, the past information can be reused to improve filtering performance in terms of the steady-state mean square error. The convergence characteristics of the filter parameters in LRKOL-MCC and KRMC-MF are also derived. Simulations on chaotic time-series prediction and nonlinear regression illustrate the desirable accuracy and robustness of the proposed filters. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2018, v. 6, p. 10540-10552 | en_US |
dcterms.isPartOf | IEEE access | en_US |
dcterms.issued | 2018 | - |
dc.identifier.scopus | 2-s2.0-85042369215 | - |
dc.identifier.eissn | 2169-3536 | en_US |
dc.description.validate | 202403 bckw | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | EIE-0944 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; China Postdoctoral Science Foundation Funded Project; Chongqing Postdoctoral Science Foundation Special Funded Project; Fundamental Research Funds for the Central Universities | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6821893 | - |
dc.description.oaCategory | VoR allowed | en_US |
Appears in Collections: | Journal/Magazine Article |
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Tse_Kernel_Adaptive_Filters.pdf | 5.38 MB | Adobe PDF | View/Open |
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