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Journal of System Simulation

Abstract

Abstract: A robust identification algorithm based on the self-join adjacent-feedback loop reservoir (SALR) network was proposed for dual-rate sampled nonlinear systems with complex nonlinear characteristics and measurement outputs containing outliers. The SALR network was applied to describe the nonlinear characteristics of the target system, and wavelet neurons were injected into the reservoir to enhance its memory and nonlinear description capabilities. The identification problem of the nonlinear system was transformed into the identification problem of the network's output weight matrix. The Huber loss function was used to construct the criterion function, and an error threshold was introduced to improve the robustness of the stochastic gradient identification algorithm against outliers. To solve the problem of output data loss caused by dual-rate sampling, the concepts of auxiliary model identification and interaction estimation theory were introduced into the recursive identification process of the output weights, where the estimated outputs of the network were used to replace the unmeasured outputs. Moreover, the whale optimization algorithm was adopted to optimize the network's hyperparameters, further enhancing the identification accuracy. Numerical simulation results validate the effectiveness of the proposed algorithm.

First Page

2652

Last Page

2661

CLC

TP273

Recommended Citation

Jiang Wenbin, Cao Yuqing, Xie Li, et al. Robust Identification of Dual-rate Sampled Nonlinear Systems Based on SALR Network[J]. Journal of System Simulation, 2025, 37(10): 2652-2661.

Corresponding Author

Xie Li

DOI

10.16182/j.issn1004731x.joss.24-0557

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