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Multi-state dependent parameter model identification and estimation

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Multi-state dependent parameter model identification and estimation. / Tych, Wlodzimierz; Sadeghi, Jafar; Smith, Paul James; Chotai, Arunkumar; Taylor, Charles James.

System Identification, Environmental Modelling, and Control System Design. ed. / Liuping Wang; Hugues Garnier. London : Springer, 2012. p. 191-210.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Tych, W, Sadeghi, J, Smith, PJ, Chotai, A & Taylor, CJ 2012, Multi-state dependent parameter model identification and estimation. in L Wang & H Garnier (eds), System Identification, Environmental Modelling, and Control System Design. Springer, London, pp. 191-210. https://doi.org/10.1007/978-0-85729-974-1_10

APA

Tych, W., Sadeghi, J., Smith, P. J., Chotai, A., & Taylor, C. J. (2012). Multi-state dependent parameter model identification and estimation. In L. Wang, & H. Garnier (Eds.), System Identification, Environmental Modelling, and Control System Design (pp. 191-210). Springer. https://doi.org/10.1007/978-0-85729-974-1_10

Vancouver

Tych W, Sadeghi J, Smith PJ, Chotai A, Taylor CJ. Multi-state dependent parameter model identification and estimation. In Wang L, Garnier H, editors, System Identification, Environmental Modelling, and Control System Design. London: Springer. 2012. p. 191-210 https://doi.org/10.1007/978-0-85729-974-1_10

Author

Tych, Wlodzimierz ; Sadeghi, Jafar ; Smith, Paul James ; Chotai, Arunkumar ; Taylor, Charles James. / Multi-state dependent parameter model identification and estimation. System Identification, Environmental Modelling, and Control System Design. editor / Liuping Wang ; Hugues Garnier. London : Springer, 2012. pp. 191-210

Bibtex

@inbook{1b636f55620240e784150b6130e50102,
title = "Multi-state dependent parameter model identification and estimation",
abstract = "This chapter describes an important generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems to include Multi-State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi-path 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 non-linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are included.",
author = "Wlodzimierz Tych and Jafar Sadeghi and Smith, {Paul James} and Arunkumar Chotai and Taylor, {Charles James}",
year = "2012",
doi = "10.1007/978-0-85729-974-1_10",
language = "English",
isbn = "9780857299734",
pages = "191--210",
editor = "Liuping Wang and Hugues Garnier",
booktitle = "System Identification, Environmental Modelling, and Control System Design",
publisher = "Springer",

}

RIS

TY - CHAP

T1 - Multi-state dependent parameter model identification and estimation

AU - Tych, Wlodzimierz

AU - Sadeghi, Jafar

AU - Smith, Paul James

AU - Chotai, Arunkumar

AU - Taylor, Charles James

PY - 2012

Y1 - 2012

N2 - This chapter describes an important generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems to include Multi-State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi-path 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 non-linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are included.

AB - This chapter describes an important generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems to include Multi-State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi-path 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 non-linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are included.

U2 - 10.1007/978-0-85729-974-1_10

DO - 10.1007/978-0-85729-974-1_10

M3 - Chapter

SN - 9780857299734

SP - 191

EP - 210

BT - System Identification, Environmental Modelling, and Control System Design

A2 - Wang, Liuping

A2 - Garnier, Hugues

PB - Springer

CY - London

ER -