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Convex regularized variable-forgetting-factor recursive least squares algorithm for sparse system identification
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  • Xin Yang,
  • Yuhong Wang,
  • Site Mo,
  • gen wang
Xin Yang
State Grid Sichuan Electric Power Company

Corresponding Author:2569362745@qq.com

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Yuhong Wang
Sichuan University
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Site Mo
Sichuan University
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gen wang
State Grid Sichuan Electric Power Company
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Abstract

A convex regularized variable-forgetting-factor recursive least squares algorithm (CR-VFFRLS) is proposed for sparse system identification, in which the variable-forgetting-factor is deduced by minimizing the convex regularized cost function via the gradient descent method. It overcomes the drawback that the fast-tracking ability with the high steady-state error or the low steady-state error with slow tracking ability, which is ineluctable in the fixed forgetting-factor RLS algorithm. Simulation results demonstrate the superiorities of the proposed algorithm to the CR-RLS and VFFRLS algorithm.