An executable and asymptotically optimal adaptive RLS algorithm

Author

Morimoto, Jiro ; Yamamoto, Yoshikazu ; Kobayashi, Ikunori ; Furumoto, Nariayo ; Tabuchi, Toshiaki

Author_Institution

Fac. of Eng., Tokushima Bunri Univ., Kagawa, Japan

fYear

1997

fDate

29-31 Jul 1997

Firstpage

1317

Lastpage

1320

Abstract

A setting method for the forgetting factor in recursive least squares (RLS) algorithms is presented. In a general adaptive setting method, to obtain the optimal value of the forgetting factor, the cost function constructed with the mean squared prediction errors is differentiated and the derivative that is set to zero is solved numerically. The problem of this method is that just one of local minimums is obtained unless the cost function is confirmed as unimodal. The main object of this contribution is to show the unimodality of the cost function of the RLS algorithm

Keywords

adaptive estimation; least squares approximations; minimisation; recursive estimation; asymptotically optimal adaptive recursive least squares algorithm; cost function; forgetting factor; mean squared prediction errors; unimodality; Additive noise; Algorithm design and analysis; Cost function; Equations; Least squares approximation; Least squares methods; Linear regression; Resonance light scattering; Time varying systems; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE '97. Proceedings of the 36th SICE Annual Conference. International Session Papers

Conference_Location

Tokushima

Type

conf

DOI

10.1109/SICE.1997.625051

Filename

625051