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Robust Standard Gradient Descent Algorithm for ARX Models Using Aitken Acceleration Technique
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DOI：10.1109/TCYB.2021.3063113.png)
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摘要
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A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration method is developed. Considering that the SGD algorithm has slow convergence rates and is sensitive to the step size, a robust and accelerative SGD (RA-SGD) algorithm is derived. This algorithm is based on the Aitken acceleration method, and its convergence rate is improved from linear convergence to at least quadratic convergence in general. Furthermore, the RA-SGD algorithm is always convergent with no limitation of the step size. Both the convergence analysis and the simulation examples demonstrate that the presented algorithm is effective.
Keyword:
Convergence
Acceleration
Mathematical model
Standards
Parameter estimation
Nonlinear equations
Computational modeling
Aitken acceleration technique
ARX model
convergence rate
parameter estimation
standard gradient descent (SGD) algorithm
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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