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
Hauptverfasser: Li, Kai, Xiao, Jun, Xie, JinFang, Wu, RuiQi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2022
Schlagworte:
Active Noise Control
Adaptive systems
Computer simulation
Convergence
Filtering algorithms
Heuristic algorithms
Informatics
RLS algorithm
Steady-state
variable forgetting factor adaptive filtering algorithm
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Zusammenfassung: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.
DOI:10.1109/ICINC58035.2022.00053