Due to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified.

由于模型本身的局限性，切换自回归外生（SARX）模型的性能在非线性混合动态系统建模时将面临潜在威胁。为了解决这个问题，本文开发了一种鲁棒的开关门控循环单元（SGRU）模型识别方法。首先，SARX模型的所有子模型都被门控循环单元神经网络取代。得到的SGRU模型比SARX模型具有更强的非线性拟合能力。其次，本文偏离了传统的噪声高斯分布假设，而是选择了广义高斯分布。这使得所提出的模型能够在不同噪声的影响下实现稳定的预测性能。值得注意的是，在所提出的切换模型中，没有对操作模式的知识强加任何先验假设。因此，本文采用EM算法来解决隐变量参数估计问题。最后进行了两次仿真实验。通过比较SGRU模型与SARX模型的非线性拟合能力以及不同噪声分布下SGRU模型的预测性能，验证了所提方法的有效性。