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A graph/particle-based method for experiment design in nonlinear systemsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Edward Boje and Xiaohua Xia, International Federation of Automatic Control , 2014, 1404-1409 p.Conference paper (Refereed)
##### Abstract [en]

##### Place, publisher, year, edition, pages

International Federation of Automatic Control , 2014. 1404-1409 p.
##### Series

, World Congress, ISSN 1474-6670 ; Volumen 19, Part 1
##### National Category

Control Engineering Signal Processing Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:liu:diva-106751DOI: 10.3182/20140824-6-ZA-1003.00361ISBN: 978-3-902823-62-5OAI: oai:DiVA.org:liu-106751DiVA: diva2:718421
##### Conference

19th IFAC World Congress, Cape Town, South Africa, August 24-29
#####

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##### Projects

Probabilistic modelling of dynamical systems
##### Funder

Swedish Research Council, 621-2013-5524
Available from: 2014-05-21 Created: 2014-05-21 Last updated: 2016-05-04Bibliographically approved
##### In thesis

We propose an extended method for experiment design in nonlinear state space models. The proposed input design technique optimizes a scalar cost function of the information matrix, by computing the optimal stationary probability mass function (pmf) from which an input sequence is sampled. The feasible set of the stationary pmf is a polytope, allowing it to be expressed as a convex combination of its extreme points. The extreme points in the feasible set of pmf’s can be computed using graph theory. Therefore, the final information matrix can be approximated as a convex combination of the information matrices associated with each extreme point. For nonlinear systems, the information matrices for each extreme point can be computed by using particle methods. Numerical examples show that the proposed techniquecan be successfully employed for experiment design in nonlinear systems.

1. Sequential Monte Carlo for inference in nonlinear state space models$(function(){PrimeFaces.cw("OverlayPanel","overlay718460",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay718460",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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