Abstract
The nonlinear system identification of block-oriented systems like Hammerstein and Wiener systems is considered. The static nonlinearity is modeled by local affine models whose input space decomposition can be fixed a-priori or can be learnt during optimization. The optimization is done in output error configuration and is solved via the Levenberg-Marquardt algorithm using finite differences. The local affine model used is LOLIMOT which is flexible and robust and allows to model all kinds of smooth and unique nonlinear functions. Furthermore, a regularization operator especially for local affine models is applied which preserves the local interpretability of the submodels during the global optimization which optimizes the parameters of all submodels and the linear dynamic subsystem simultaneously.
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