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

Hua Chen, Yuejiang Ji and Daniele Salvati

Complexity, 2022, vol. 2022, 1-10

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9244890

DOI: 10.1155/2022/9244890

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