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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2020

12.03.2019 | Original Article

Novel computing paradigms for parameter estimation in power signal models

verfasst von: Ammara Mehmood, Naveed Ishtiaq Chaudhary, Aneela Zameer, Muhammad Asif Zahoor Raja

Erschienen in: Neural Computing and Applications | Ausgabe 10/2020

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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.
Vorheriger Artikel A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems
Nächster Artikel Multi-scale multi-block covariance descriptor with feature selection

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Metadaten
Titel
Novel computing paradigms for parameter estimation in power signal models
verfasst von
Ammara Mehmood
Naveed Ishtiaq Chaudhary
Aneela Zameer
Muhammad Asif Zahoor Raja
Publikationsdatum
12.03.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04133-9

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