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access icon free Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle

  • Author(s): Ling Xu 1, 2  and  Feng Ding 2
    • View affiliations
    • Affiliations: 1: School of Internet of Things Technology, Wuxi Vocational Institute of Commerce, Wuxi 214153, People's Republic of China ;
      2: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China
  • Source: Volume 11, Issue 2, April 2017, p. 228 – 237
    DOI:  10.1049/iet-spr.2016.0220 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 17/04/2016, Accepted 07/09/2016, Revised 02/09/2016, Published 13/09/2016

In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques.

Inspec keywords: signal processing; stochastic processes; estimation theory; gradient methods; parameter estimation

Other keywords: dynamical response signal; hierarchical principle; gradient search; scalar innovation; innovation vector; multiinnovation stochastic gradient algorithm; industrial process; stochastic disturbance; hierarchical identification algorithm; interaction estimation theory; parameter estimation algorithm; multiinnovation theory; nonlinear dynamical system; decomposition technique

Subjects: Simulation, modelling and identification; Signal processing theory; Other topics in statistics; Optimisation techniques; Optimisation techniques; Signal processing and detection; Other topics in statistics

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