Authors:
Yue Zhao
1
;
Kueiming Lo
1
and
Wook-Hyun Kwon
2
Affiliations:
1
School of Software, Tsinghua University, Key Lab for ISS, MOE China, China
;
2
School of Electrical Engineering, Seoul National University, Korea, Republic of
Keyword(s):
Multi-dimensional system, identification criterion, ARMAX model, recursive algorithm.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Optimization Problems in Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
Time Series and System Modeling
Abstract:
Most system recursive identification algorithms are based on the prediction error (PE) criterion. Such a recursive algorithm only considers the present estimation residual error instead of all estimation residuals. It would result in large estimation error when the signal noise disturbs strongly. In this paper, a new identification criterion is proposed. It considers both the errors between the actual outputs and the estimation result and the difference of each estimation error. Under this criterion, a new recursive algorithm MSDCN (Multi-dimensional System Disturbed by Color Noise) is proposed. For multi-dimensional systems, weighting different values on the estimation errors and the difference of each error, MSDCN could both decrease the estimation errors and got smooth prediction curves. Several simulation examples are given to illustrate the method’s anti-disturbance performance.