Identification of Switched Gated Recurrent Neural Networks Using the EM Algorithm - Bai, Wentao; Gu, Suhang; Yan, Chao; Zhang, Haoyu; Jiang, Chunli; Guo, Fan (2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS), 2023)
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.