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Novel computing paradigms for parameter estimation in power signal models

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Abstract

The strength of evolutionary computational heuristic paradigms is exploited for parameter estimation of power signal modeling problems by incorporating differential evolution (DE), genetic algorithms (GAs) and pattern search (PS) methodologies. The objective function of power signal harmonics is constructed by utilizing the power of approximation theory in mean squared error sense. The stiff optimization task of signal harmonics is performed with heuristic solvers DE, GAs and PS that provide efficacy, fast convergence rate and avoid getting trapped in local minima. Statistics reveal that DE outperforms its counterparts in terms of accuracy, robustness and complexity measures.

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Authors and Affiliations

  1. Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Ammara Mehmood

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

    Naveed Ishtiaq Chaudhary

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

    Aneela Zameer

  4. Department of Electrical Engineering, COMSATS Institute of Information Technology, Attock, Pakistan

    Muhammad Asif Zahoor Raja

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

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Mehmood, A., Chaudhary, N.I., Zameer, A. et al. Novel computing paradigms for parameter estimation in power signal models. Neural Comput & Applic 32, 6253–6282 (2020). https://doi.org/10.1007/s00521-019-04133-9

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  • DOI: https://doi.org/10.1007/s00521-019-04133-9

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