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Parameter Tuning in the Single-Solution Simulated Kalman Filter Optimizer

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Intelligent Manufacturing and Mechatronics (SympoSIMM 2019)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Single-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.

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References

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Acknowledgments

This research is supported by the Fundamental Research Grant Scheme awarded by the Ministry of Higher Education Malaysia to Universiti Malaysia Pahang (RDU170106).

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Authors and Affiliations

  1. Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia

    Nor Hidayati Abdul Aziz & Nor Azlina Ab Aziz

  2. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Melaka, Malaysia

    Nor Hidayati Abdul Aziz, Zuwairie Ibrahim, Badaruddin Muhammad & Tasiransurini Ab Rahman

  3. Faculty of Manufacturing, Universiti Malaysia Pahang, Melaka, Malaysia

    Zuwairie Ibrahim & Suhazri Amrin Rahmad

  4. Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Melaka, Malaysia

    Mohd Saberi Mohamad

Authors
  1. Nor Hidayati Abdul Aziz
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  2. Zuwairie Ibrahim
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  3. Nor Azlina Ab Aziz
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  4. Badaruddin Muhammad
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  5. Tasiransurini Ab Rahman
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  6. Mohd Saberi Mohamad
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  7. Suhazri Amrin Rahmad
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Corresponding author

Correspondence to Zuwairie Ibrahim .

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Editors and Affiliations

  1. Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Zamberi Jamaludin

  2. Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Mohd Najib Ali Mokhtar

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Abdul Aziz, N.H. et al. (2020). Parameter Tuning in the Single-Solution Simulated Kalman Filter Optimizer. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds) Intelligent Manufacturing and Mechatronics. SympoSIMM 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-9539-0_5

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  • DOI: https://doi.org/10.1007/978-981-13-9539-0_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9538-3

  • Online ISBN: 978-981-13-9539-0

  • eBook Packages: EngineeringEngineering (R0)

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