Details

Autor(en) / Beteiligte

• Tang, Jia
• Ben Makhlouf, Abdellatif
• Abdellatif Ben Makhlouf

Titel

Fractional Gradient Descent-Based Auxiliary Model Algorithm for FIR Models with Missing Data

Ist Teil von

• Complexity (New York, N.Y.), 2023, Vol.2023, p.1-12

Ort / Verlag

Hoboken: Hindawi

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• This study proposes a fractional gradient descent (FGD) algorithm for FIR models with missing data. By using the auxiliary model method, the missing data can be obtained. Then, the FGD algorithm is applied to update the parameters of the FIR models. Because of the fractional term in the conventional GD algorithm, the convergence rates of the GD algorithm can be increased. In addition, to avoid the step-size calculation, an Aitken FGD-based auxiliary model algorithm is also introduced. The convergence analysis and simulation examples are provided to show the effectiveness of the proposed algorithms.