Fractional Order Differentiators Design Using Honey Badger Optimization Algorithm Based s to z Transform
Wireless Personal Communications, ISSN: 1572-834X, Vol: 139, Issue: 3, Page: 1565-1591
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This paper’s main objective is to create a digital fractional order differentiator (DFOD) that is precise, wideband, and stable. First, the Honey Badger algorithm (HBA) has been used to build the first order s to z transform by minimizing the L1-norm based error function. Performance comparison of designs based on real coded genetic algorithms (RCGA) and differential evolution (DE) and HBA-based first order transformations. Later, the indirect discretization of the new s to z transform using continuing fraction expansion (CFE) was used to develop the fourth and fifth orders for half and one-third fractional order differentiators. The RCGA and DE-based designs are contrasted with the relative magnitude error (RME) analysis of DFODs utilizing the HBA-based transform. The suggested approach performs better in terms of its magnitude response when compared to the current methods, demonstrating the superiority of the suggested HBA-based DFODs. The maximum absolute RME values of the fifth order for half and one-third of DFODs were obtained as -46.27dB and -49.12dB respectively.
Bibliographic Details
Springer Science and Business Media LLC
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