Improved Stochastic Recurrent Networks for Nonlinear State Space System Identification
Xinpeng Liu, Xiaocong Du, Xianqiang Yang, Chao Cai · 2023
This paper presents an improved version of the stochastic recurrent networks (STORN) for identification of non-linear state space systems with more complex model structures, including long short-term memory network (LSTM) and bidirectional gated recurrent unit (BiGRU). Both LSTM and BiGRU are recurrent neural networks with multiple state variables, which can store different information in the modeling of sequential data. Applying such a priori information into the prediction of data distribution can lead to better model performance. In this paper, several information fusion techniques are compared, and the effectiveness of the method is verified on three benchmark identification datasets. Our model outperforms the state-of-the-art baseline by 0.3 percent with 8 times fewer parameters.