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

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 searc...

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Vydané v:IET signal processing Ročník 11; číslo 2; s. 228 - 237
Hlavní autori: Xu, Ling, Ding, Feng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 01.04.2017
Predmet:
Algorithms
decomposition technique
dynamical response signal
Dynamical systems
Estimates
estimation theory
gradient methods
gradient search
hierarchical identification algorithm
hierarchical principle
industrial process
Innovation
innovation vector
interaction estimation theory
multiinnovation stochastic gradient algorithm
multiinnovation theory
nonlinear dynamical system
Parameter estimation
parameter estimation algorithm
Parameters
Research Article
scalar innovation
Scalars
signal processing
stochastic disturbance
stochastic processes
Stochasticity
estimation theory
signal processing
parameter estimation algorithm
decomposition technique
dynamical response signal
industrial process
multiinnovation stochastic gradient algorithm
interaction estimation theory
nonlinear dynamical system
scalar innovation
innovation vector
gradient search
parameter estimation
stochastic disturbance
stochastic processes
gradient methods
multiinnovation theory
hierarchical principle
hierarchical identification algorithm
ISSN:1751-9675, 1751-9683, 1751-9683
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2016.0220