A Bayesian approach to the identification of piece-wise linear output error models

A.L. Juloski; S. Weiland

A Bayesian approach to the identification of piece-wise linear output error models

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In this paper we develop an algorithm for the identification of piecewise linear output error models for the case where the discrete mode of the underlying hybrid system is not known. The presented algorithm is based on a Bayesian framework, i.e. unknown model parameters are treated as random variables and described with probability density functions. The identification problem is posed as a problem of computing the posterior parameter densities, given the prior densities and the observed data. A suboptimal identification algorithm is derived. Operation of the algorithm is demonstrated on an example.

Original language	English
Title of host publication	Proceedings of the 14th IFAC Symposium on System Identification (SYSID-2006) March 29-31, 2006, Newcastle, Australia : Vol 14.
Editors	B. Ninness, H. Hjalmarsson
Publication status	Published - 2006
Event	14th IFAC Symposium on System Identification (SYSID 2006) - Newcastle, Australia Duration: 29 Mar 2006 → 31 Mar 2006 Conference number: 14

Conference

Conference	14th IFAC Symposium on System Identification (SYSID 2006)
Abbreviated title	SYSID 2006
Country/Territory	Australia
City	Newcastle
Period	29/03/06 → 31/03/06

Cite this

@inproceedings{4e615aa03a244ea8a83a85e8dff1ed49,

title = "A Bayesian approach to the identification of piece-wise linear output error models",

abstract = "In this paper we develop an algorithm for the identification of piecewise linear output error models for the case where the discrete mode of the underlying hybrid system is not known. The presented algorithm is based on a Bayesian framework, i.e. unknown model parameters are treated as random variables and described with probability density functions. The identification problem is posed as a problem of computing the posterior parameter densities, given the prior densities and the observed data. A suboptimal identification algorithm is derived. Operation of the algorithm is demonstrated on an example.",

author = "A.L. Juloski and S. Weiland",

year = "2006",

language = "English",

editor = "B. Ninness and H. Hjalmarsson",

booktitle = "Proceedings of the 14th IFAC Symposium on System Identification (SYSID-2006) March 29-31, 2006, Newcastle, Australia : Vol 14.",

note = "14th IFAC Symposium on System Identification (SYSID 2006), SYSID 2006 ; Conference date: 29-03-2006 Through 31-03-2006",

}

A Bayesian approach to the identification of piece-wise linear output error models. / Juloski, A.L.; Weiland, S.
Proceedings of the 14th IFAC Symposium on System Identification (SYSID-2006) March 29-31, 2006, Newcastle, Australia : Vol 14.. ed. / B. Ninness; H. Hjalmarsson. 2006.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A Bayesian approach to the identification of piece-wise linear output error models

AU - Juloski, A.L.

AU - Weiland, S.

N1 - Conference code: 14

PY - 2006

Y1 - 2006

N2 - In this paper we develop an algorithm for the identification of piecewise linear output error models for the case where the discrete mode of the underlying hybrid system is not known. The presented algorithm is based on a Bayesian framework, i.e. unknown model parameters are treated as random variables and described with probability density functions. The identification problem is posed as a problem of computing the posterior parameter densities, given the prior densities and the observed data. A suboptimal identification algorithm is derived. Operation of the algorithm is demonstrated on an example.

AB - In this paper we develop an algorithm for the identification of piecewise linear output error models for the case where the discrete mode of the underlying hybrid system is not known. The presented algorithm is based on a Bayesian framework, i.e. unknown model parameters are treated as random variables and described with probability density functions. The identification problem is posed as a problem of computing the posterior parameter densities, given the prior densities and the observed data. A suboptimal identification algorithm is derived. Operation of the algorithm is demonstrated on an example.

M3 - Conference contribution

BT - Proceedings of the 14th IFAC Symposium on System Identification (SYSID-2006) March 29-31, 2006, Newcastle, Australia : Vol 14.

A2 - Ninness, B.

A2 - Hjalmarsson, H.

T2 - 14th IFAC Symposium on System Identification (SYSID 2006)

Y2 - 29 March 2006 through 31 March 2006

ER -

A Bayesian approach to the identification of piece-wise linear output error models

Abstract

Conference

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