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Design of backtracking search heuristics for parameter estimation of power signals

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Abstract

This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, Australia

    Ammara Mehmood & Peng Shi

  2. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Muhammad Asif Zahoor Raja

  3. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC

    Muhammad Asif Zahoor Raja

  4. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Aneela Zameer

  5. Department of Electrical Engineering, International Islamic University Islamabad, Islamabad, Pakistan

    Naveed Ishtiaq Chaudhary

Authors
  1. Ammara Mehmood
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  2. Peng Shi
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  3. Muhammad Asif Zahoor Raja
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  4. Aneela Zameer
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  5. Naveed Ishtiaq Chaudhary
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Correspondence to Ammara Mehmood.

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Mehmood, A., Shi, P., Raja, M.A.Z. et al. Design of backtracking search heuristics for parameter estimation of power signals. Neural Comput & Applic 33, 1479–1496 (2021). https://doi.org/10.1007/s00521-020-05029-9

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  • DOI: https://doi.org/10.1007/s00521-020-05029-9

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