Journal
IET SIGNAL PROCESSING
Volume 7, Issue 8, Pages 646-654Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-spr.2012.0183
Keywords
gradient methods; least squares approximations; parameter estimation; recursive estimation; signal processing; stochastic processes; noise model parameter algorithm; system model parameter algorithm; BJ system decomposition; two-stage multiinnovation stochastic gradient method; two-stage recursive least-square identification method; Box-Jenkins systems; two-stage parameter estimation algorithms
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Funding
- National Natural Science Foundation of China [61273194]
- Natural Science Foundation of Jiangsu Province (China) [BK2012549]
- PAPD of Jiangsu Higher Education Instituttions
- 111 Project [B12018]
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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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