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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Published in:2022 International Conference on Informatics, Networking and Computing (ICINC) pp. 228 - 232
Main Authors: Li, Kai, Xiao, Jun, Xie, JinFang, Wu, RuiQi
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2022
Subjects:
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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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
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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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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
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