Convex regularized variable-forgetting-factor recursive least squares
algorithm for sparse system identification
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.