Adaptive bilinear predictors

Author

Lee, Junghsi ; Mathews, V. John

Author_Institution

Comput. & Commun. Res. Lab., Ind. Technol. Res. Inst., Hsinchu, Taiwan

Volume

iii

fYear

1994

fDate

19-22 Apr 1994

Abstract

This paper considers an extended recursive least squares (RLS) adaptive bilinear predictor. It is shown that the extended RLS adaptive bilinear predictor is guaranteed to be stable in the sense that the time average of the squared a-posteriori prediction error signal is bounded whenever the input signal is bounded in the same sense. It also shows that the a-priori prediction error itself is bounded whenever the desired signal is bounded. This paper also contains simulation results to demonstrate the usefulness of the extended RLS adaptive bilinear predictor

Keywords

adaptive signal processing; bilinear systems; least squares approximations; prediction theory; recursive estimation; stability; time series; a-priori prediction error; bilinear time series; extended RLS adaptive bilinear predictor; input signal; prediction error signal; recursive least squares; simulation results; squared a-posteriori prediction error; stability results; time average; Biological system modeling; Control system synthesis; Difference equations; Ear; Economic forecasting; Filtering; Nonlinear systems; Polynomials; Resonance light scattering; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389983

Filename

389983