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

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2 Author(s)
Wen-Xiao Zhao ; Key Lab. of Syst. & Control, Acad. of Math. & Syst. Sci., Beijing, China ; Han-Fu Chen

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 Ak = A + εk with εk → 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.

Published in:

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

Date of Conference:

6-8 July 2012

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