Fractional gradient descent algorithms for systems with outliers: A matrix fractional derivative or a scalar fractional derivative

Yuan Cao and Shuai Su

Chaos, Solitons & Fractals, 2023, vol. 174, issue C

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.

Keywords: Parameter estimation; Fractional based method; Outliers; Convergence rate; Computational efforts (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007828

DOI: 10.1016/j.chaos.2023.113881

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