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M. Kozek, S. Sinanovic:
"Identification of Wiener models using optimal local linear models";
Talk: 6th International EUROSIM Congress, Ljubljana, Slovenia; 2007-09-09 - 2007-09-13; in: "Proceedings of the 6th EUROSIM Congress on Modelling and Simulation", Vol.2 (2007), ISBN: 978-3-901608-32-2; Paper ID 350, 6 pages.

The Wiener model is a versatile nonlinear block oriented model structure for different applications. In this paper a method for identifying the parameters of such a model using optimal local linear models is presented. The optimality of the proposed algorithm is threefold: First, each local model is linear in the parameters and therefore optimal parameter estimation methods like Recursive Least-Squares can be applied thus leading to a robust solution. Second, the region of validity of each local model is adaptively optimized using the Chi-squared distribution of the estimated residual. This approach not only enables an automatic choice of the model size but it also incorporates the measurement noise level of the output variable into the result. And third, the resulting global model has a minimum of local models while guaranteeing optimal performance. A simulation of the pharmacological Propofol model is included, which documents the ability of the algorithm to balance the output noise with the systems nonlinearity.

nonlinear identification, Wiener model, optimality, local linear models.

http://publik.tuwien.ac.at/files/pub-mb_5536.pdf