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...
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| Veröffentlicht in: | 2022 International Conference on Informatics, Networking and Computing (ICINC) S. 228 - 232 |
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| Sprache: | Englisch |
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01.10.2022
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| 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. |
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| 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 |
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| Snippet | This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor... |
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| 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 |
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