Novel computing paradigms for parameter estimation in power signal models

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 con...

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Bibliographic Details
Published in:Neural computing & applications 2020-05, Vol.32 (10), p.6253-6282
Main Authors: Mehmood, Ammara, Chaudhary, Naveed Ishtiaq, Zameer, Aneela, Raja, Muhammad Asif Zahoor
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Harmonics
Image Processing and Computer Vision
Mathematical models
Optimization
Original Article
Parameter estimation
Pattern search
Probability and Statistics in Computer Science
Solvers
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04133-9