Journal
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
Volume 37, Issue 5, Pages 1884-1906Publisher
SPRINGER BIRKHAUSER
DOI: 10.1007/s00034-017-0636-0
Keywords
Iterative algorithm; Parameter estimation; Least squares; Multivariable system; Auxiliary model
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Funding
- Natural Science Foundation of Shandong Province (China) [ZR2016FL08]
- Science Foundation of Jining University (China) [2016QNKJ01]
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This paper considers the parameter estimation of a multiple-input-output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.
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