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access icon free Two-stage parameter estimation algorithms for Box–Jenkins systems

  • Author(s): Feng Ding 1, 2  and  Honghong Duan 1
    • View affiliations
    • Affiliations: 1: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, People's Republic of China;
      2: Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122, People's Republic of China
  • Source: Volume 7, Issue 8, October 2013, p. 646 – 654
    DOI:  10.1049/iet-spr.2012.0183 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 25/06/2012, Accepted 22/04/2013, Revised 22/12/2012, Published

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.

Inspec keywords: signal processing; stochastic processes; gradient methods; parameter estimation; least squares approximations; recursive estimation

Other keywords: system model parameter algorithm; two-stage multiinnovation stochastic gradient method; BJ system decomposition; noise model parameter algorithm; Box-Jenkins systems; two-stage parameter estimation algorithms; two-stage recursive least-square identification method

Subjects: Other topics in statistics; Signal processing theory; Signal processing and detection; Interpolation and function approximation (numerical analysis); Other topics in statistics; Interpolation and function approximation (numerical analysis)

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