Hysteresis System Identification Using Preisach and Prandtl-Ishlinskii Models Based Neural Network

Hysteresis system identification is a research topic in nonlinear system identification of long history. This paper proposes a novel recurrent neural network architecture to carry out hysteresis system identification. We first modify the original Prandtl-Ishlinskii (PI) model with one threshold parameter to a PI model with two threshold parameters, and then develop recurrent neural network based on the hybrid of the modified PI model and the Preisach model. We then evaluate our proposed network on systems simulated by different models, and explores the potential of model based neural networks for hysteresis system identification. As shown by numerical simulations, the proposed recurrent neural network can achieve better performances in describing different kinds of hysteresis systems.