A Novel EM Identification Method for Hammerstein Systems With Missing Output Data

This article concerns a novel auxiliary-model-based expectation maximization (EM) estimation method for Hammerstein systems with data loss by extending the EM method to estimate models with multiple parameter vectors. The novel EM method relaxes the requirements on an autoregression model with one parameter vector, interactively maximizes the expectation over multiple parameter vectors in a more general model, and uses the output of an auxiliary model to substitute the missing outputs in the information vector in iteration processes. A numerical simulation is employed to demonstrate the effectiveness of the proposed novel EM method.