Stochastic approximation based PCA and its application to identification of EIV systems

Author

Zhao, Wen-Xiao ; Chen, Han-Fu

Author_Institution

Key Lab. of Syst. & Control, Acad. of Math. & Syst. Sci., Beijing, China

fYear

2012

fDate

6-8 July 2012

Firstpage

3276

Lastpage

3280

Abstract

The stochastic approximation based principal component analysis (SAPCA) algorithm is introduced to recursively estimate the eigenvectors and the corresponding eigenvalues of a symmetric matrix A based on observations A_k = A + ε_k with ε_k → 0 as k → ∞. The estimates are strongly consistent. The SAPCA algorithm is then applied to identifying the matrix coefficients of the multivariate errors-in-variables (EIV) systems, and the estimates are also strongly consistent. The performance of SAPCA algorithm is testified by a simulation example.

Keywords

eigenvalues and eigenfunctions; matrix algebra; principal component analysis; stochastic processes; EIV system identification; PCA; eigenvalues; eigenvectors; matrix coefficients; multivariate errors-in-variables systems; principal component analysis; stochastic approximation; symmetric matrix; Convergence; Eigenvalues and eigenfunctions; Equations; Mathematical model; Signal processing algorithms; Symmetric matrices; Vectors; Stochastic approximation; errors-in-variables system; principal component analysis; recursive identification; strong consistency;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6358438

Filename

6358438