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Content Provider | IET Digital Library |
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Author | Ding, Feng Duan, Honghong |
Abstract | A two-stage recursive least-squares identification method and a two-stage multi-innovation stochastic gradient method are derived for Box–Jenkins (BJ) systems. The key is to decompose a BJ system into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model, and then to estimate the parameters of the system model and the noise model, respectively. The simulation examples indicate that the proposed algorithms can generate highly accurate parameter estimates and require small computational burden. |
Starting Page | 646 |
Ending Page | 654 |
Page Count | 9 |
ISSN | 17519675 |
Volume Number | 7 |
e-ISSN | 17519683 |
Issue Number | Issue 8, Oct (2013) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/7/8 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2012.0183 |
Journal | IET Signal Processing |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | BJ System Decomposition Box-Jenkins System Gradient Method Interpolation And Function Approximation Least Squares Approximation Noise Model Parameter Algorithm Numerical Analysis Parameter Estimation Recursive Estimation Signal Processing Signal Processing And Detection Signal Processing Theory Statistics Stochastic Linearised SCUC System Model Parameter Algorithm Two-stage Multiinnovation Stochastic Gradient Method Two-stage Parameter Estimation Algorithm Two-stage Recursive Least-square Identification Method |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering |
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