Efficient total least squares method for system modeling using minor component analysis

Author

Rao, Yadunandana N. ; Principe, Jose C.

Author_Institution

Computational NeuroEngineering Lab., Florida Univ., Gainesville, FL, USA

fYear

2002

fDate

2002

Firstpage

259

Lastpage

268

Abstract

We present two algorithms to solve the total least-squares (TLS) problem. The algorithms are on-line with O(N²) and O(N) complexity. The convergence of the algorithms is significantly faster than the traditional methods. A mathematical analysis of convergence is also provided along with simulations to substantiate the claims. We also apply the TLS algorithms for FIR system identification with known model order in the presence of noise.

Keywords

FIR filters; computational complexity; convergence of numerical methods; eigenvalues and eigenfunctions; filtering theory; least squares approximations; parameter estimation; signal processing; FIR filters; FIR system identification; TLS algorithms; complexity; convergence; efficient total least squares method; minor component analysis; minor eigenvector; on-line algorithms; parameter estimation; signal processing; simulations; system modeling; Algorithm design and analysis; Analytical models; Convergence; Laboratories; Least squares methods; Modeling; Neural engineering; Parameter estimation; Signal processing algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on

Print_ISBN

0-7803-7616-1

Type

conf

DOI

10.1109/NNSP.2002.1030037

Filename

1030037