Recursive identification for multi-channel Hammerstein systems

Abstract

This paper considers the recursive identification for multi-channel Hammerstein systems with internal noises and observation noise, where the linear parts are ARX systems. With the help of the correlations of system inputs and output, the recursive algorithms are proposed for estimating unknown coefficients of the linear subsystem, while the system nonlinearity is recursively estimated by using the kernel functions. Strong consistency of the estimates is proved under reasonable conditions, and a simulation example is provided.

Recursive identification for semiparametric multi-channel wiener systems

“…For the widely use of the additive models in various problems of statistical inference refer to [7] for example. This structure is often referred to as the multi-channel system or additive system [8,9]. For MIMO Wiener systems, the resulting models are multi-channel or additive Wiener systems.…”