Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 354, Issue 3, Pages 1321-1339Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2016.11.030
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Funding
- National Natural Science Foundation of China [61273194, 61471162]
- Flexible Distinguished Top-Level Talent Plan of Jiangxi Province Talent Project 555
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This paper develops a decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm requires less computational efforts. The simulation results show that the proposed algorithms can work well. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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