An Auxiliary-Model-Based Stochastic Gradient Algorithm for Dual-Rate Sampled-Data Box–Jenkins Systems

This paper presents an identification algorithm for Box–Jenkins systems by combining the auxiliary model identification idea and the gradient search principle. The proposed algorithm can estimate all unknown parameters of the Box–Jenkins systems. Furthermore, to improve the convergence rate of the s...

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Bibliographic Details
Published inCircuits, systems, and signal processing Vol. 32; no. 5; pp. 2475 - 2485
Main Authors Chen, Jing, Ding, Rui
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.10.2013
Springer Nature B.V
Subjects
Algorithms
Circuits
Circuits and Systems
Computer simulation
Convergence
Electrical Engineering
Electronics and Microelectronics
Engineering
Estimates
Instrumentation
Parameter estimation
Searching
Short Paper
Signal processing
Signal,Image and Speech Processing
Stochastic models
Stochasticity
Parameter estimation
Box-Jenkins system
Auxiliary model
Stochastic gradient
Dual-rate system
Online AccessGet full text
ISSN0278-081X
1531-5878
DOI10.1007/s00034-013-9563-x

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Summary:This paper presents an identification algorithm for Box–Jenkins systems by combining the auxiliary model identification idea and the gradient search principle. The proposed algorithm can estimate all unknown parameters of the Box–Jenkins systems. Furthermore, to improve the convergence rate of the stochastic gradient algorithm, a modified stochastic gradient algorithm is given. The simulation results indicate that the proposed algorithm can work well.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-013-9563-x