Varying Infimum Gradient Descent Algorithm for Agent-Sever Systems Using Different Order Iterative Preconditioning Methods

Chen, Jing; Wang, Dongqing; Liu, Yanjun; Zhu, Quanmin

doi:10.1109/tii.2021.3123304

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…Inspired by the PSO algorithm and the power method [29], we propose two modified GD algorithms for timedelayed models: one is an exhaustive search method which chooses the step size based on the PSO algorithm, and the other is the power-based GD algorithm which computes the step size using power method. ese two algorithms can get a better step size in each iteration without eigenvalue calculation.…”

Section: Introductionmentioning

confidence: 99%

Exhaustive Search and Power‐Based Gradient Descent Algorithms for Time‐Delayed FIR Models

Chen

2022

Complexity

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In this study, two modified gradient descent (GD) algorithms are proposed for time-delayed models. To estimate the parameters and time-delay simultaneously, a redundant rule method is introduced, which turns the time-delayed model into an augmented model. Then, two GD algorithms can be used to identify the time-delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: (1) avoid a high-order matrix eigenvalue calculation, thus, are more efficient for large-scale systems; (2) have faster convergence rates, therefore, are more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms.

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Section: Introductionmentioning

confidence: 99%