A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.