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• W4385501504 abstract "Two gradient descent based fractional methods are proposed for systems with outliers in this paper. The outliers in the collected data usually causes biased estimates, resulting in a poor identification model. Tradition fractional gradient descent (FGD) algorithm has an assumption that the fractional derivative is a scalar, which leads to slow convergence rates, especially for systems with an ill-conditioned matrix. The proposed algorithms in this paper have several advantages over the traditional identification methods: (1) can get unbiased estimates; (2) have faster convergence rates; (3) enrich the FGD estimation framework. Simulation examples demonstrate the effectiveness of the proposed algorithms." @default.
• W4385501504 created "2023-08-03" @default.
• W4385501504 creator A5014601688 @default.
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• W4385501504 date "2023-09-01" @default.
• W4385501504 modified "2023-09-27" @default.
• W4385501504 title "Fractional gradient descent algorithms for systems with outliers: A matrix fractional derivative or a scalar fractional derivative" @default.
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• W4385501504 doi "https://doi.org/10.1016/j.chaos.2023.113881" @default.
• W4385501504 hasPublicationYear "2023" @default.
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