Identification of Switched Gated Recurrent Neural Networks Using the EM Algorithm

In the domain of nonlinear hybrid dynamic system modeling, the effectiveness of switched autoregressive exogenous (SARX) systems may face certain restrictions. To address this issue, this paper presents an enhanced switched system framework. In this framework, all SARX subsystems are replaced with g...

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Bibliographic Details
Published in2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) pp. 1 - 6
Main Authors Bai, Wentao, Gu, Suhang, Yan, Chao, Zhang, Haoyu, Jiang, Chunli, Guo, Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.09.2023
Subjects
EM algorithm
gated recurrent neural networks
Heuristic algorithms
hybrid dynamic systems
Logic gates
Probabilistic logic
Recurrent neural networks
SARX
Switched systems
Switches
Switching systems
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DOI10.1109/DOCS60977.2023.10294958

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Summary:In the domain of nonlinear hybrid dynamic system modeling, the effectiveness of switched autoregressive exogenous (SARX) systems may face certain restrictions. To address this issue, this paper presents an enhanced switched system framework. In this framework, all SARX subsystems are replaced with gated recurrent neural networks, aiming to overcome these limitations. Importantly, the proposed switched system does not rely on any prior assumptions about the knowledge of operating modes. Finally, a new identification method is proposed based on the expectation-maximization (EM) algorithm, and its effectiveness is validated through a simulation example.
DOI:10.1109/DOCS60977.2023.10294958