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Combined regularization parameter for normalized LMS algorithm and its performance analysis.
- Source :
-
Signal Processing . Sep2019, Vol. 162, p75-82. 8p. - Publication Year :
- 2019
-
Abstract
- • The combined regularization parameter for normalized least-mean-square (CRP-NLMS) algorithm is proposed, in which the mixing parameter is derived by minimizing the energy of noise-free a posteriori error. • A novel reset method is designed to improve the tracking capability of the proposed algorithm. We also illustrate that the proposed algorithm is available for the nonstationary system modeled by a random walk process. • The theoretical analyses including the transient and steady-state mean-square error (MSE) are performed in a stationary system. This paper presents a combined regularization parameter for normalized least-mean-square (CRP-NLMS) algorithm. The proposed algorithm adaptively combines two different regularization parameters by employing a time-varying mixing parameter that is derived by minimizing the energy of noise-free a posteriori error. To avoid large fluctuations, the mixing parameter is updated in a moving-average method. A novel reset method is designed to improve the tracking capability when the unknown system encounters a sudden change. We illustrate that the proposed mixing parameter is also available for the nonstationary system modeled by a random walk process. In particular, the theoretical analyses including the transient and steady-state mean-square error (MSE) are performed. Simulations for system identification scenarios demonstrate the merits of Kour finding and support the theoretical analysis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 162
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 136416850
- Full Text :
- https://doi.org/10.1016/j.sigpro.2019.04.014