State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter

Mohamadreza Ahmadi, Hamed Mojallali, Roozbeh Izadi-Zamanabadi

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

14 Citationer (Scopus)

Resumé

This paper proposes a new version of the particle filtering (PF) algorithm based on the invasive weed optimization (IWO) method. The sub-optimality of the sampling step in the PF algorithm is prone to estimation errors. In order to avert such approximation errors, this paper suggests applying the IWO algorithm by translating the sampling step into a nonlinear optimization problem. By introducing an appropriate fitness function, the optimization problem is properly treated. The validity of the proposed method is evaluated against three distinct examples: the stochastic volatility estimation problem in finance, the severely nonlinear waste water sludge treatment plant, and the benchmark target tracking on re-entry problem. By simulation analysis and evaluation, it is verified that applying the suggested IWO enhanced PF algorithm (PFIWO) would contribute to significant estimation performance improvements.
OriginalsprogEngelsk
TidsskriftSwarm and Evolutionary Computation
Vol/bind4
Sider (fra-til)44-53
Antal sider11
ISSN2210-6502
DOI
StatusUdgivet - 2012

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Nonlinear Stochastic Systems
Particle Filtering
Stochastic systems
Particle Filter
State Estimation
State estimation
Metaheuristics
Optimization Problem
Reentry
Wastewater Treatment
Stochastic Volatility
Target Tracking
Approximation Error
Simulation Analysis
Estimation Error
Fitness Function
Nonlinear Optimization
Finance
Optimization Methods
Nonlinear Problem

Bibliografisk note

Swarm and Evolutionary Computation, Elsevier, Vol. 4, pp. 44-53, 2012.

Citer dette

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State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter. / Ahmadi, Mohamadreza ; Mojallali, Hamed ; Izadi-Zamanabadi, Roozbeh.

I: Swarm and Evolutionary Computation, Bind 4, 2012, s. 44-53.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter

AU - Ahmadi, Mohamadreza

AU - Mojallali, Hamed

AU - Izadi-Zamanabadi, Roozbeh

N1 - Swarm and Evolutionary Computation, Elsevier, Vol. 4, pp. 44-53, 2012.

PY - 2012

Y1 - 2012

N2 - This paper proposes a new version of the particle filtering (PF) algorithm based on the invasive weed optimization (IWO) method. The sub-optimality of the sampling step in the PF algorithm is prone to estimation errors. In order to avert such approximation errors, this paper suggests applying the IWO algorithm by translating the sampling step into a nonlinear optimization problem. By introducing an appropriate fitness function, the optimization problem is properly treated. The validity of the proposed method is evaluated against three distinct examples: the stochastic volatility estimation problem in finance, the severely nonlinear waste water sludge treatment plant, and the benchmark target tracking on re-entry problem. By simulation analysis and evaluation, it is verified that applying the suggested IWO enhanced PF algorithm (PFIWO) would contribute to significant estimation performance improvements.

AB - This paper proposes a new version of the particle filtering (PF) algorithm based on the invasive weed optimization (IWO) method. The sub-optimality of the sampling step in the PF algorithm is prone to estimation errors. In order to avert such approximation errors, this paper suggests applying the IWO algorithm by translating the sampling step into a nonlinear optimization problem. By introducing an appropriate fitness function, the optimization problem is properly treated. The validity of the proposed method is evaluated against three distinct examples: the stochastic volatility estimation problem in finance, the severely nonlinear waste water sludge treatment plant, and the benchmark target tracking on re-entry problem. By simulation analysis and evaluation, it is verified that applying the suggested IWO enhanced PF algorithm (PFIWO) would contribute to significant estimation performance improvements.

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KW - Sub-optimal filtering

KW - Nonlinear state estimation

KW - Invasive weed optimization

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JO - Swarm and Evolutionary Computation

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