Gradient-based iterative parameter estimation for Box–Jenkins systems

This paper presents a gradient-based iterative identification algorithms for Box–Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at...

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Vydané v:Computers & mathematics with applications (1987) Ročník 60; číslo 5; s. 1200 - 1208
Hlavní autori: Wang, Dongqing, Yang, Guowei, Ding, Ruifeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2010
Predmet:
Algorithms
Box–Jenkins models
Computation
Iterative algorithms
Iterative methods
Mathematical analysis
Mathematical models
Parameter estimation
Recursive dentification
Regression
Signal processing
Stochastic gradient
Stochasticity
Signal processing
Parameter estimation
Recursive dentification
Iterative algorithms
Box–Jenkins models
Stochastic gradient
ISSN:0898-1221, 1873-7668
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Shrnutí:This paper presents a gradient-based iterative identification algorithms for Box–Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. An example is given.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2010.06.001