H∞ optimality and a posteriori output estimate of the forgetting factor NLMS algorithm

• Autores: Kiyoshi Nishiyama
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 75, 2017, págs. 244-248
• Idioma: inglés
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• Resumen

  • Abstract The normalized least mean squares (NLMS) algorithm is widely used for adaptive filtering. The NLMS algorithm may be extended using a variety of weight parameters that improve its performance. One such extension involves appropriately introducing a forgetting factor into the NLMS algorithm using the H ∞ framework. The resultant forgetting factor NLMS (FFNLMS) algorithm may be regarded as a special case of the improved proportionate NLMS (IPNLMS) algorithm. This work reveals that the FFNLMS algorithm is H ∞ -optimal, and the a posteriori output estimate is identical to the observation signal for sufficiently large times.