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Wiener model-based system identification using moth flame optimised Kalman filter algorithm

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Abstract

The primary aim of this paper is to pursue the optimal set of Wiener system coefficients using the meta-heuristic optimisation technique-based Kalman filter (KF). The best estimation of unknown parameters of a dynamic system by the basic KF approach majorly depends upon the efficiently tuned KF parameters such as process and observation noise covariance matrices; otherwise, it would lead to descent of the filter performance. To defeat the aforesaid difficulty, an advanced optimisation technique called the moth flame optimisation (MFO) algorithm is used. In this work, the proposed method proceeds through the following two steps for unknown Wiener system parameters estimation. Initially, the MFO algorithm generates the near-global optimised KF parameters by designing the suitable cost function. Finally, the unknown Wiener model parameters are determined by using the basic KF approach based on optimised KF parameters derived in the earlier step. The reliability, accuracy and robustness of the proposed MFO–KF approach are analysed on a benchmark numerical problem and three practical nonlinear plants. The results indicate that the MFO–KF method shows better estimations compared to the other employed benchmark optimisation algorithms-based KF methods as well as results from other recently reported works.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492010, India

    Lakshminarayana Janjanam & Suman Kumar Saha

  2. Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Rajib Kar & Durbadal Mandal

Authors
  1. Lakshminarayana Janjanam
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  2. Suman Kumar Saha
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  3. Rajib Kar
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  4. Durbadal Mandal
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Correspondence to Lakshminarayana Janjanam.

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Janjanam, L., Saha, S.K., Kar, R. et al. Wiener model-based system identification using moth flame optimised Kalman filter algorithm. SIViP 16, 1425–1433 (2022). https://doi.org/10.1007/s11760-021-02096-w

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  • DOI: https://doi.org/10.1007/s11760-021-02096-w

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