Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

doi:10.23919/JCC.ja.2022-0367

Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

2024-10-01

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DOI：10.23919/JCC.ja.2022-0367

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The proportionate recursive least squares (PRLS) algorithm has shown faster convergence and better performance than both proportionate updating (PU) mechanism based least mean squares (LMS) algorithms and RLS algorithms with a sparse regularization term. In this paper, we propose a variable forgetting factor (VFF) PRLS algorithm with a sparse penalty, e.g., l(1)-norm, for sparse identification. To reduce the computation complexity of the proposed algorithm, a fast implementation method based on dichotomous coordinate descent (DCD) algorithm is also derived. Simulation results indicate superior performance of the proposed algorithm.

Keywords:

dichotomous coordinate descent

proportionate matrix

RLS

sparse systems

variable forgetting factor

Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

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