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Publications (8 of 8) Show all publications
Galrinho, M., Everitt, N. & Hjalmarsson, H. (2017). Incorporating noise modeling in dynamic networks using non-parametric models. IFAC-PapersOnLine, 50(1), 10568-10573
Open this publication in new window or tab >>Incorporating noise modeling in dynamic networks using non-parametric models
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 10568-10573Article in journal (Refereed) Published
Abstract [en]

For identification of systems in dynamic networks, two-stage and instrumental variable methods are common time-domain methods. These methods provide consistent estimates of a chosen module of the network without estimating other parts of the network or noise models. However, disregarding noise modeling may come at a cost in estimation error. To capture the noise contribution, we propose the following procedure: first, we estimate a non-parametric model of an appropriate part of the network; second, we estimate the module of interest using signals simulated with the non-parametric model. The simulated signals are derived from an asymptotic maximum likelihood criterion. Preliminary simulations suggest that the propose method is competitive with existing approaches and is particularly beneficial with colored noise.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
least-squares identification, networks, System identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-223070 (URN)10.1016/j.ifacol.2017.08.1302 (DOI)000423965100254 ()2-s2.0-85031794790 (Scopus ID)
Funder
Swedish Research Council, 2015-05285, 2016-06079
Note

QC 20180213

Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-03-05Bibliographically approved
Everitt, N. (2015). Identification of Modules in Acyclic Dynamic Networks A Geometric Analysis of Stochastic Model Errors. (Licentiate dissertation). Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Identification of Modules in Acyclic Dynamic Networks A Geometric Analysis of Stochastic Model Errors
2015 (English)Licentiate thesis, monograph (Other academic)
Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. p. xi, 121
Series
TRITA-EE, ISSN 1653-5146 ; 2015:005
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-159698 (URN)978-91-7595-432-5 (ISBN)
Presentation
2015-02-11, E3, Osquarsbacke 14, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20150209

Available from: 2015-02-09 Created: 2015-02-09 Last updated: 2015-02-10Bibliographically approved
Everitt, N., Bottegal, G., Rojas, C. R. & Hjalmarsson, H. (2015). On the Effect of Noise Correlation in Parameter Identification of SIMO Systems. IFAC-PapersOnLine, 48(28), 326-331
Open this publication in new window or tab >>On the Effect of Noise Correlation in Parameter Identification of SIMO Systems
2015 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 48, no 28, p. 326-331Article in journal (Refereed) Published
Abstract [en]

The accuracy of identified linear time-invariant single-input multi-output (SIMO) models can be improved when the disturbances affecting the output measurements are spatially correlated. Given a linear parametrization of the modules composing the SIMO structure, we show that the correlation structure of the noise sources and the model structure of the othe modules determine the variance of a parameter estimate. In particular we show that increasing the model order only increases the variance of other modules up to a point. We precisely characterize the variance error of the parameter estimates for finite model orders. We quantify the effect of noise correlation structure, model structure and signal spectra.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Identification (control systems), Correlation structure, Effect of noise, Linear parametrization, Linear time invariant, Noise source, Parameter estimate, Single input multi outputs, Variance error, Parameter estimation
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-195438 (URN)10.1016/j.ifacol.2015.12.148 (DOI)2-s2.0-84988474760 (Scopus ID)
Note

QC 20161118

Available from: 2016-11-18 Created: 2016-11-03 Last updated: 2016-11-18Bibliographically approved
Everitt, N., Bottegal, G., Rojas, C. R. & Hjalmarsson, H. (2015). On the Variance Analysis of identified Linear MIMO Models. In: IEEE Explore: . Paper presented at 54th IEEE Conference on Decision and Control, Osaka, 2015. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>On the Variance Analysis of identified Linear MIMO Models
2015 (English)In: IEEE Explore, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper, Published paper (Refereed)
Abstract [en]

We study the accuracy of identified linear time-invariant multi-input multi-output (MIMO) systems. Under a stochastic framework, we quantify the effect of the spatial correlation and choice of model structure on the covariance matrix of the transfer function estimates. In particular, it is shown how the variance of a transfer function estimate depends on signal properties and model orders of other modules composing the MIMO system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
Keywords
system identification
National Category
Engineering and Technology
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-186211 (URN)10.1109/CDC.2015.7402414 (DOI)000381554501099 ()2-s2.0-84962018333 (Scopus ID)
Conference
54th IEEE Conference on Decision and Control, Osaka, 2015
Funder
Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
Note

QC 20160511

Available from: 2016-05-04 Created: 2016-05-04 Last updated: 2016-12-22Bibliographically approved
Mårtensson, J., Everitt, N. & Hjalmarsson, H. (2015). Variance Analysis in SISO Linear Systems Identification. Automatica
Open this publication in new window or tab >>Variance Analysis in SISO Linear Systems Identification
2015 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Article in journal (Refereed) Submitted
Abstract [en]

Causal single input single output linear time invariant systems are considered. Expressions for the asymptotic (co)variance of system properties estimated using the prediction error method are derived. These expressions delineate the impacts of model structure, model order, true system dynamics, and experimental conditions. A connection to results on frequency function estimation is established. Also, simple model structure independent upper bounds are derived. Explicit variance expressions and bounds are provided for common system properties such as impulse response coefficients and non-minimum phase zeros. As an illustration of the insights the expressions provide, they are used to derive conditions on the input spectrum which makethe asymptotic variance of non-minimum phase zero estimates independent of the model order and model structure.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-159099 (URN)
Funder
Swedish Research Council, 621-2007-6271Swedish Research Council, 621-2009-4017
Note

NQC 2015

Available from: 2015-01-21 Created: 2015-01-21 Last updated: 2017-12-05Bibliographically approved
Everitt, N., Rojas, C. R. & Hjalmarsson, H. (2014). Variance Results for Parallel Cascade Serial Systems. In: Proceedings of 19th IFAC World Congress: . Paper presented at IFAC 2014, 19th World Congress of the International Federation of Automatic Control.
Open this publication in new window or tab >>Variance Results for Parallel Cascade Serial Systems
2014 (English)In: Proceedings of 19th IFAC World Congress, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Modelling dynamic networks is important in different fields of science. At present, little is known about how different inputs and sensors contribute to the statistical properties concerning an estimate of a specific dynamic system in a network. We consider two forms of parallel serial structures, one multiple-input-multiple-output structure and one single-input multiple-output structure. The quality of the estimated models is analysed by means of the asymptotic covariance matrix, with respect to input signal characteristics, noise characteristics, sensor locations and previous knowledge about the remaining systems in the network. It is shown that an additive property applies to the information matrix for the considered structures. The impact of input signal selection, sensor locations and incorporation of previous knowledge isillustrated by simple examples.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-159092 (URN)
Conference
IFAC 2014, 19th World Congress of the International Federation of Automatic Control
Funder
Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
Note

QC 20150306

Available from: 2015-01-21 Created: 2015-01-21 Last updated: 2015-03-06Bibliographically approved
Everitt, N., Rojas, C. R. & Hjalmarsson, H. (2013). A Geometric Approach to Variance Analysis of Cascaded Systems. In: Proceedings of the 52nd Conference On Decision And Control: . Paper presented at 52nd IEEE Conference on Decision and Control, CDC 2013; Florence; Italy; 10 December 2013 through 13 December 2013 (pp. 6496-6501). IEEE conference proceedings
Open this publication in new window or tab >>A Geometric Approach to Variance Analysis of Cascaded Systems
2013 (English)In: Proceedings of the 52nd Conference On Decision And Control, IEEE conference proceedings, 2013, p. 6496-6501Conference paper, Published paper (Refereed)
Abstract [en]

Modeling complex and interconnected systems is a key issue in system identification. When estimating individual subsystems of a network of interconnected system, it is of interest to know the improvement of model-accuracy in using different sensors and actuators. In this paper, using a geometric approach, we quantify the accuracy improvement from additional sensors when estimating the first of a set of subsystems connected in a cascade structure. We present results on how the zeros of the first subsystem affect the accuracy of the corresponding model. Additionally we shed some light on how structural properties and experimental conditions determine the accuracy. The results are particularized to FIR systems, for which the results are illustrated by numerical simulations. A surprising special case occurs when the first subsystem contains a zero on the unit circle; as the model orders grows large, thevariance of the frequency function estimate, evaluated at thecorresponding frequency of the unit-circle zero, is shown to be the same as if the other subsystems were completely known.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
Keywords
System Identification, Asymptotic covariance, Cascaded systems, Structured identification, Identification of dynamic networks
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-138167 (URN)000352223507054 ()2-s2.0-84902308632 (Scopus ID)978-146735717-3 (ISBN)
Conference
52nd IEEE Conference on Decision and Control, CDC 2013; Florence; Italy; 10 December 2013 through 13 December 2013
Funder
Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
Note

QC 20150703

Available from: 2013-12-18 Created: 2013-12-18 Last updated: 2015-12-08Bibliographically approved
Everitt, N., Bottegal, G., Rojas, C. R. & Hjalmarsson, H. Variance Analysis of Linear SIMO Models with Spatially Correlated Noise.
Open this publication in new window or tab >>Variance Analysis of Linear SIMO Models with Spatially Correlated Noise
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Substantial improvement in accuracy of identied linear time-invariant single-input multi-output (SIMO) dynamical models ispossible when the disturbances aecting the output measurements are spatially correlated. Using an orthogonal representation for the modules composing the SIMO structure, in this paper we show that the variance of a parameter estimate of a module is dependent on the model structure of the other modules, and the correlation structure of the disturbances. In addition, we quantify the variance-error for the parameter estimates for finite model orders, where the effect of noise correlation structure, model structure and signal spectra are visible. From these results, we derive the noise correlation structure under which the mentioned model parameterization gives the lowest variance, when one module is identied using less parameters than the other modules.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-159094 (URN)
Funder
Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
Note

QS 2015

Available from: 2015-01-21 Created: 2015-01-21 Last updated: 2015-02-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1127-1397

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