A novel adaptive variable forgetting factor RLS algorithm
This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor and the error signal based on the Sigmoid function. The algorithm overcomes the conflict problem of steady-state performance and dynamic perfo...
Uloženo v:
| Vydáno v: | 2022 International Conference on Informatics, Networking and Computing (ICINC) s. 228 - 232 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.10.2022
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor and the error signal based on the Sigmoid function. The algorithm overcomes the conflict problem of steady-state performance and dynamic performance of the fixed forgetting factor RLS algorithm. The forgetting factor gradually increases during the gradual convergence of the algorithm, which ensures the algorithm's steady-state performance while accelerating the algorithm's tracking speed and convergence speed. At the same time, this paper also analyzes the rules of the parameters α and β and the effects of the parameters α and β on the performance of the RLS algorithm. Finally, computer simulations are conducted, and the results are consistent with the theoretical analysis, confirming that the algorithm outperforms the traditional RLS algorithm. |
|---|---|
| AbstractList | This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor and the error signal based on the Sigmoid function. The algorithm overcomes the conflict problem of steady-state performance and dynamic performance of the fixed forgetting factor RLS algorithm. The forgetting factor gradually increases during the gradual convergence of the algorithm, which ensures the algorithm's steady-state performance while accelerating the algorithm's tracking speed and convergence speed. At the same time, this paper also analyzes the rules of the parameters α and β and the effects of the parameters α and β on the performance of the RLS algorithm. Finally, computer simulations are conducted, and the results are consistent with the theoretical analysis, confirming that the algorithm outperforms the traditional RLS algorithm. |
| Author | Li, Kai Xie, JinFang Xiao, Jun Wu, RuiQi |
| Author_xml | – sequence: 1 givenname: Kai surname: Li fullname: Li, Kai email: leesi@whut.edu.cn organization: Wuhan University of Technology,School of Mechanical and Electronic Engineering,Wuhan,China – sequence: 2 givenname: Jun surname: Xiao fullname: Xiao, Jun email: 1285997861@qq.com organization: Wuhan University of Technology,School of Mechanical and Electronic Engineering,Wuhan,China – sequence: 3 givenname: JinFang surname: Xie fullname: Xie, JinFang email: 417312610@QQ.com organization: Hubei Institute of Measurement and Testing Technology,Wuhan,China – sequence: 4 givenname: RuiQi surname: Wu fullname: Wu, RuiQi email: 928913631@qq.com organization: Wuhan University of Technology,School of Mechanical and Electronic Engineering,Wuhan,China |
| BookMark | eNotzM1KxDAUQOEIutBx3kAhL9B6kzRtshyKP4Wi4Izr4aa9qYFOM8RQ8O0VlLP4dueGXS5xIcbuBZRCgH3o2u611QaULiVIWQKAVhdsaxtrlAYFtrZwzeyOL3GlmeOI5xxW4iumgG4m7mOaKOewTNzjkGPi7_2e4zzFFPLn6ZZdeZy_aPvvhn08PR7al6J_e-7aXV8EIWwusKFaNQaVB5QwGu0Hh3VN2jfCKQIcR1d5U0lJiOD8b4JsNVQk9GDcqDbs7u8biOh4TuGE6fsoACwIadUP9chGaQ |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICINC58035.2022.00053 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798350309690 |
| EndPage | 232 |
| ExternalDocumentID | 10090129 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i119t-a7e6378a3f0a20d85fcba66e5f71b3e0addb4f8422eaa0bfbfb1e94c4e15c8bd3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 27 02:21:09 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i119t-a7e6378a3f0a20d85fcba66e5f71b3e0addb4f8422eaa0bfbfb1e94c4e15c8bd3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10090129 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Oct. |
| PublicationDateYYYYMMDD | 2022-10-01 |
| PublicationDate_xml | – month: 10 year: 2022 text: 2022-Oct. |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 International Conference on Informatics, Networking and Computing (ICINC) |
| PublicationTitleAbbrev | ICINC |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8085402 |
| Snippet | This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 228 |
| SubjectTerms | Active Noise Control Adaptive systems Computer simulation Convergence Filtering algorithms Heuristic algorithms Informatics RLS algorithm Steady-state variable forgetting factor adaptive filtering algorithm |
| Title | A novel adaptive variable forgetting factor RLS algorithm |
| URI | https://ieeexplore.ieee.org/document/10090129 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JSwMxFA62ePCkYsWdHLyOZrJMkqMUi4KU4gK9lSwvWqgzpbb9_SaZulw8SC4hl_AS8ra8730IXTLpQrAaCiskLbgVrjBemqKyXHCjvdK-JZuQw6Eaj_VoA1bPWBgAyMVncJWm-S_fN26VUmXxhROdEicd1JGyasFaG1ROSfT1fT8GwkIRJmLcR3MjzsR5_Is1JRuNwe4_t9tDvR_4HR59G5Z9tAX1AdI3uG7WMMPGm3nSUXgdw9wEfMIhp7VT_TJu6XPw48MTNrPXJkb-b-899DK4fe7fFRveg2JalnpZGAkVk8qwQAwlXongrKkqEEGWlgGJKsnyoDilYAyxIY4SNHccSuGU9ewQdeumhiOEDdXR46KBOha4V84wbaITl_wu4rjix6iX5J7M29YWky-RT_5YP0U76WjbarYz1F0uVnCOtt16Of1YXOQL-QSCWY7V |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JSwMxFA5aBT2pWHE3B6-jmSwzyVGKpcVailborWR50UKdKbXt7zeZqcvFg-QScgkvIW_L-96H0DXLrfdGQWJEThNuhE20y3WSGS64Vk4qV5NN5P2-HI3UYA1Wr7AwAFAVn8FNnFZ_-a60y5gqCy-cqJg42URbgnNKarjWGpeTEnXbbYVQWEjCRIj8aNWKM7Ie_-JNqcxGe--fG-6j5g8ADw--TcsB2oDiEKk7XJQrmGLt9CxqKbwKgW6EPmFfJbZjBTOuCXTwU-8Z6-lrGWL_t_cmemnfD1udZM18kEzSVC0SnUPGcqmZJ5oSJ4W3RmcZCJ-nhgEJSslwLzmloDUxPowUFLccUmGlcewINYqygGOENVXB56KeWua5k1YzpYMbFz0vYrnkJ6gZ5R7P6uYW4y-RT_9Yv0I7neFjb9zr9h_O0G485rq27Rw1FvMlXKBtu1pMPuaX1eV8AiJ8khw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+International+Conference+on+Informatics%2C+Networking+and+Computing+%28ICINC%29&rft.atitle=A+novel+adaptive+variable+forgetting+factor+RLS+algorithm&rft.au=Li%2C+Kai&rft.au=Xiao%2C+Jun&rft.au=Xie%2C+JinFang&rft.au=Wu%2C+RuiQi&rft.date=2022-10-01&rft.pub=IEEE&rft.spage=228&rft.epage=232&rft_id=info:doi/10.1109%2FICINC58035.2022.00053&rft.externalDocID=10090129 |