Identification for Wiener-Hammerstein systems under quantized inputs and quantized output observations

This paper investigates the Wiener-Hammerstein system identification with quantized inputs and quantized output observations. By parameterizing the static nonlinear function, system identifiability is discussed first. Then, for the identifiable system a three-step algorithm is proposed to estimate the unknown parameters by employing the empirical measure-based method and the quasi-convex combination technique. Finally, the algorithm is proved to be strongly convergent, the mean-square convergence rate is presented, and the asymptotic efficiency is given by selecting a suitable transformation matrix. A numerical simulation is included to demonstrate the main results obtained.