Open AccessParameter estimation algorithms for dynamical response signals based on the multi‐innovation theory and the hierarchical principle
Author(s) -
Xu Ling,
Ding Feng
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0220
Subject(s) - estimation theory , scalar (mathematics) , algorithm , identification (biology) , computer science , dynamical systems theory , system identification , process (computing) , identification scheme , scheme (mathematics) , mathematics , mathematical optimization , data modeling , mathematical analysis , botany , geometry , physics , quantum mechanics , database , biology , operating system
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.