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Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems.

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Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems. / Sadeghi, Jafar; Tych, W.; Chotai, A. et al.
In: Electronics Letters, Vol. 46, No. 18, 09.2010, p. 1265-1266.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Sadeghi J, Tych W, Chotai A, Young PC. Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems. Electronics Letters. 2010 Sept;46(18):1265-1266. doi: 10.1049/el.2010.1180

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Sadeghi, Jafar ; Tych, W. ; Chotai, A. et al. / Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems. In: Electronics Letters. 2010 ; Vol. 46, No. 18. pp. 1265-1266.

Bibtex

@article{57596987cd8143d993b38af8c5d22ce1,
title = "Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems.",
abstract = "An important generalisation of the state dependent parameter approach to the modelling of nonlinear dynamic systems to include multi-state dependent parameter (MSDP) nonlinearities is described. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multipath trajectory, employing the Kalman filter and fixed interval smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for its use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of nonlinear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design.",
author = "Jafar Sadeghi and W. Tych and A. Chotai and Young, {P. C.}",
year = "2010",
month = sep,
doi = "10.1049/el.2010.1180",
language = "English",
volume = "46",
pages = "1265--1266",
journal = "Electronics Letters",
issn = "0013-5194",
publisher = "Institution of Engineering and Technology",
number = "18",

}

RIS

TY - JOUR

T1 - Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems.

AU - Sadeghi, Jafar

AU - Tych, W.

AU - Chotai, A.

AU - Young, P. C.

PY - 2010/9

Y1 - 2010/9

N2 - An important generalisation of the state dependent parameter approach to the modelling of nonlinear dynamic systems to include multi-state dependent parameter (MSDP) nonlinearities is described. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multipath trajectory, employing the Kalman filter and fixed interval smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for its use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of nonlinear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design.

AB - An important generalisation of the state dependent parameter approach to the modelling of nonlinear dynamic systems to include multi-state dependent parameter (MSDP) nonlinearities is described. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multipath trajectory, employing the Kalman filter and fixed interval smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for its use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of nonlinear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design.

U2 - 10.1049/el.2010.1180

DO - 10.1049/el.2010.1180

M3 - Journal article

VL - 46

SP - 1265

EP - 1266

JO - Electronics Letters

JF - Electronics Letters

SN - 0013-5194

IS - 18

ER -