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A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR)

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Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering

Abstract

Particle filters are recursive Bayesian estimators, which are being applied to many areas of engineering in recent years to estimate states and parameters, regarding fire spread, tumors, oil pipelines, heat transfer, chemical reactors, etc. The key idea behind particle filters is that they use an initial distribution (sample), based on the previous state estimate, to calculate the best estimate for the current state, relying only on the current available measurements and the knowledge about the system. The greatest advantage of these methods is the easy computational implementation. However, setting the standard deviation for the initial distribution is very important for the success of the method. For this reason, standard formulation of these methods may not provide good results in problems with large discontinuities (or irregular/abrupt changes). For example, this would be the case of estimating step changes in the heat flux on a plate. Although several solutions have been proposed to improve the estimation performance, they still suffer from the curse of discontinuity. This occurs because particle filters proposed in the literature are not adaptive methods. In the example mentioned above, particle filters can have both a priori information and sample satisfactory before the change. However, after the change begins, the available information could be not enough to draw a suitable sample for the estimation. At this point, it is necessary to modify the standard deviation to broaden the particle search field or to move the a priori information to a new region where a new sample should be drawn. In this regard, the aim of this chapter is to propose a hybrid estimation scheme based on Particle Swarm Optimization (PSO) built into the particle filter Sampling Importance Resampling (SIR) to project the a priori information to a new search region, according to the current observation. To demonstrate the proposal, the problem of estimating step changes on the heat flux on a plate is taken into account, considering experimental measurements. The results allow to state that the scheme combining PSO and SIR provides good performance for this type of problem.

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Acknowledgements

The authors acknowledge the financial support provided by FAPERJ–Fundação Carlos Chagas Filho de Amparo à Pesquissa do Estado do Rio de Janeiro, CNPq–Conselho Nacional de Desenvolvimento Científico e Tecnológico, and CAPES–Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, research supporting agencies from Brazil.

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Authors and Affiliations

  1. Chemical Engineering Program, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil

    Wellington Betencurte da Silva & Julio Cesar Sampaio Dutra

  2. Statistics Department, Federal University of Amazonas, Manaus, Brazil

    José Mir Justino da Costa

  3. Department of Mechanical Engineering and Energy, Polytechnic Institute, IPRJ-UERJ, Nova Friburgo, Brazil

    Luiz Alberto da Silva Abreu, Diego Campos Knupp & Antônio José Silva Neto

Authors
  1. Wellington Betencurte da Silva
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  2. Julio Cesar Sampaio Dutra
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  3. José Mir Justino da Costa
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  4. Luiz Alberto da Silva Abreu
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  5. Diego Campos Knupp
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  6. Antônio José Silva Neto
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Editors and Affiliations

  1. Universidade Federal do Rio Grande, Santo Antônio da Patrulha, Rio Grande do Sul, Brazil

    Gustavo Mendes Platt

  2. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  3. Polytechnic Institute, Universidade do Estado do Rio de Janeiro, Nova Friburgo, Rio de Janeiro, Brazil

    Antônio José Silva Neto

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da Silva, W.B., Dutra, J.C.S., Costa, J.d., Abreu, L.A.d.S., Knupp, D.C., Silva Neto, A.J. (2019). A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR). In: Platt, G., Yang, XS., Silva Neto, A. (eds) Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-96433-1_13

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