Article ID: | iaor20121966 |
Volume: | 63 |
Issue: | 5 |
Start Page Number: | 975 |
End Page Number: | 984 |
Publication Date: | Mar 2012 |
Journal: | Computers and Mathematics with Applications |
Authors: | Ding Ruifeng, Yao Guoyu |
Keywords: | optimization, statistics: regression, matrices |
A two‐stage least squares based iterative (two‐stage LSI) identification algorithm is derived for controlled autoregressive moving average (CARMA) systems. The basic idea is to decompose a CARMA system into two subsystems and to identify each subsystem, respectively. Because the dimensions of the involved covariance matrices in each subsystem become small, the proposed algorithm has a high computational efficiency. The simulation results indicate that the proposed algorithm is effective.