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A variable regularization parameter widely linear complex-valued NLMS algorithm: Performance analysis and wind prediction
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DOI：10.1016/j.sigpro.2022.108731.png)
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Abstract
En 中文
To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have op-timal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. The proposed algorithm can adaptively change the regularized parameter by exploiting a time-varying parameter that is obtained via making the power of noise-free a posteriori error minimum. A proper es-timated method is provided to compute the power of the measured noise when the noise is unknown, and the moving-average method is employed to update the regularized parameter for avoiding large fluc-tuations. Then we provide the analysis of the transient and steady-state (TAS) behaviors of the proposed algorithm. Simulation results with different input signals illustrate that the VRP-WL-CNLMS algorithm has better advantages than other algorithms, and verify the theoretical validity of TAS analysis of the pro-posed algorithm in the system identification (SI) environment. Finally, the experimental results of wind prediction show that the predicted value of the proposed algorithm has a smaller error value with the original signal than the WL-CNLMS algorithm and can predict the signal better that can support the su-periority of the proposed algorithm as well. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
A posteriori error
Variable regularization parameter
Approximate correlating transform