system identification using fast euclidean direction search based adaptive algorithm

the recursive least squares RLS algorithm has established itself as the ultimate adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior unfortunately practical implementations of the algorithm are often associated with high computationla complexity and/or poor numerical propeties rather that focusing on full RLS algorithm implementations aiming directly at remedying these problems we argue that the use of simplified or partial RLS algorithms may be a viable alternative to full RLS in particular we point out that the fast euclidian direction search FEDS can indeed be interpreted as such partial RLS algorithms exhibitinga nice tradeoff between complexity and performance .