Identification of sparse FIR systems using a general quantisation scheme

Boris I. Godoy; Juan C. Agüero; Rodrigo Carvajal; Graham C. Goodwin; Juan I. Yuz

doi:10.1080/00207179.2013.861611

Identification of sparse FIR systems using a general quantisation scheme

Boris I. Godoy, Juan C. Agüero, Rodrigo Carvajal, Graham C. Goodwin, Juan I. Yuz

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ℓ₁ a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.

Original language	English
Pages (from-to)	874-886
Number of pages	13
Journal	International Journal of Control
Volume	87
Issue number	4
DOIs	https://doi.org/10.1080/00207179.2013.861611
State	Published - 3 Apr 2014
Externally published	Yes

Keywords

Maximum likelihood
Quantised systems
Sparsity
System identification

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10.1080/00207179.2013.861611

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title = "Identification of sparse FIR systems using a general quantisation scheme",

abstract = "This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ℓ1 a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.",

keywords = "Maximum likelihood, Quantised systems, Sparsity, System identification",

author = "Godoy, {Boris I.} and Ag{\"u}ero, {Juan C.} and Rodrigo Carvajal and Goodwin, {Graham C.} and Yuz, {Juan I.}",

note = "Funding Information: The authors wish to thank the anonymous reviewers and the associate editor for their careful reading and helpful comments. Funding This work was partially supported by Chile{\textquoteright}s National Commission for Scientific and Technological Research (CONICYT) through its postdoctoral fellowship programme 2011, and grant no. ACT-053.",

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doi = "10.1080/00207179.2013.861611",

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T1 - Identification of sparse FIR systems using a general quantisation scheme

AU - Godoy, Boris I.

AU - Agüero, Juan C.

AU - Carvajal, Rodrigo

AU - Goodwin, Graham C.

AU - Yuz, Juan I.

N1 - Funding Information: The authors wish to thank the anonymous reviewers and the associate editor for their careful reading and helpful comments. Funding This work was partially supported by Chile’s National Commission for Scientific and Technological Research (CONICYT) through its postdoctoral fellowship programme 2011, and grant no. ACT-053.

PY - 2014/4/3

Y1 - 2014/4/3

N2 - This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ℓ1 a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.

AB - This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ℓ1 a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.

KW - Maximum likelihood

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KW - Sparsity

KW - System identification

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Identification of sparse FIR systems using a general quantisation scheme

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Keywords

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