Title :
Noise-induced bias in PCA modeling of linear system
Author :
Cao, Jin ; Gertler, Janos
Author_Institution :
Dept. of Electr. & Comput. Eng., George Mason Univ., Fairfax, VA, USA
Abstract :
The estimation of parameter bias caused by noise is very significant for modeling and identification. The effect of noise in least squares (LS) identification is well known. Recent studies on modeling by principal component analysis (PCA) have raised the problem of noise-induced bias in this framework. In this paper, a systematical investigation of this topic in static linear systems is presented. Analytical expressions of the bias induced by actuator and sensor noise in PCA modeling are derived, and an approach to approximate these expressions is developed for practical use. The theoretical results are verified by simulation results
Keywords :
eigenvalues and eigenfunctions; fault diagnosis; least squares approximations; linear systems; parameter estimation; principal component analysis; MIMO system; eigenvalues; estimation bias; fault detection; fault diagnosis; identification; least squares; linear systems; modeling; parameter estimation; principal component analysis; Actuators; Fault detection; Fault diagnosis; Least squares approximation; Least squares methods; Linear systems; Parameter estimation; Principal component analysis; Redundancy; Vectors;
Conference_Titel :
American Control Conference, 2001. Proceedings of the 2001
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-6495-3
DOI :
10.1109/ACC.2001.946203
