|
|
|
|
|
Two-Stage Least Squares based Iterative Identification Algorithm for Box-Jenkins Model |
|
PP: 1355-1360 |
|
Author(s) |
|
Jie Jia,
Hua Huang,
Yong Yang,
Ke Lv,
Feng Ding,
|
|
Abstract |
|
In order to improve the parameters estimation precision, a two-stage least squares iterative algorithm for Box-Jenkins models
is presented, which is based on the interactive estimation theory of the hierarchical identification and the auxiliary model. The main
idea of the algorithm is to decompose a Box-Jenkins system into two subsystems so as to identify each subsystem, respectively. In our
algorithm, the dimensions of the involved covariance matrices in each subsystem turn to be small. The simulation results indicate that
the proposed algorithm is effective and has a high computational efficiency. |
|
|
|
|
|