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.
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