Two-stage parameter estimation algorithms for Box–Jenkins systems
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 para...
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| Published in | IET signal processing Vol. 7; no. 8; pp. 646 - 654 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Stevenage
The Institution of Engineering and Technology
01.10.2013
Institution of Engineering and Technology John Wiley & Sons, Inc |
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| Online Access | Get full text |
| ISSN | 1751-9675 1751-9683 1751-9683 |
| DOI | 10.1049/iet-spr.2012.0183 |
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| 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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Duan, Honghong Ding, Feng |
| Author_xml | – sequence: 1 givenname: Feng surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: 2Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 2 givenname: Honghong surname: Duan fullname: Duan, Honghong organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, People's Republic of China |
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| Keywords | least squares approximations system model parameter algorithm signal processing Box-Jenkins systems two-stage parameter estimation algorithms two-stage recursive least-square identification method noise model parameter algorithm two-stage multiinnovation stochastic gradient method BJ system decomposition recursive estimation parameter estimation stochastic processes gradient methods Parameter estimation Innovation Multistage method Simulation Least squares method Subsystem Recursive method Stochastic method Algorithm Gradient method |
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| Snippet | A two-stage recursive least-squares identification method and a two-stage multi-innovation stochastic gradient method are derived for Box–Jenkins (BJ) systems.... A two‐stage recursive least‐squares identification method and a two‐stage multi‐innovation stochastic gradient method are derived for Box–Jenkins (BJ) systems.... A two-stage recursive least-squares identification method and a two-stage multi-innovation stochastic gradient method are derived for Box-Jenkins (BJ) systems.... |
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| SubjectTerms | Algorithms Applied sciences BJ system decomposition Box‐Jenkins systems Computer simulation Detection, estimation, filtering, equalization, prediction Estimates Exact sciences and technology gradient methods Information, signal and communications theory least squares approximations Least squares method Mathematical models Noise noise model parameter algorithm parameter estimation Recursive recursive estimation Signal and communications theory Signal processing Signal, noise stochastic processes system model parameter algorithm Telecommunications and information theory two‐stage multiinnovation stochastic gradient method two‐stage parameter estimation algorithms two‐stage recursive least‐square identification method |
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| Title | Two-stage parameter estimation algorithms for Box–Jenkins systems |
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