A new approach to introducing a forgetting factor into the normalized least mean squares algorithm

作者：Nishiyama Kiyoshi^*

来源：Signal Processing, 2015, 114: 19-23.

DOI：10.1016/j.sigpro.2015.02:012

• Adaptive filter
• Forgetting factor
• NLMS algorithm
• RLS algorithm
• H-infinity filter
• System identification

The performances of adaptive filtering algorithms are critically controlled by specific tunable parameters. The convergence rate of the normalized least mean squares (NLMS) algorithm may be accelerated by adjusting the step size parameter. The tracking speed of the recursive least squares (RLS) algorithm may be improved by using the forgetting factor, which has not yet been appropriately introduced into the NLMS algorithm. This work aims to successfully introduce the forgetting factor into the NLMS algorithm using an tic, theoretical framework developed to create a unified view of adaptive algorithms for recursively identifying the finite impulse response (FIR) filter coefficients. The performances of the forgetting factor NLMS (FFNLMS) algorithm developed here, in the context of several adaptive filtering applications, are evaluated using computer simulations.

• 出版日期2015-9