An RLS algorithm with evolving forgetting factor

Sheng Zhang, Jiashu Zhang

Abstract: This paper presents a novel recursive least squares (RLS) algorithm which automatically determines its forgetting factor by an evolutionary method. The evolutionary method increases or decreases the forgetting factor by comparing the output error with a threshold. The experimental results show that the proposed algorithm has fast convergence speed and small steady-state error compared to the RLS.