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M. Kozek, S. Helm, C. Hametner:
"A Novel Criterion for Optimal Identification of Wiener Models Using Local Linear Models";
Talk: 12th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2008, Orlando, Florida, USA; 2008-06-29 - 2008-07-02; in: "Prodeedings of the WMSCI 2008", (2008), ISBN: 978-1-934272-49-7; Paper ID S759TQ, 6 pages.

A novel criterion is presented for the optimal choice
of local linear models representing a Wiener model. The linear
part is modeled by a transfer function and the nonlinear
(possibly not invertible or discontinuous) function is modeled
by a series of local linear models. The algorithm is based on a
recursive approach where the novel criterion is applied to test the
local models for adhering a predefined accuracy. The criterion
automatically adapts to existing output noise levels and delivers
an optimal model with respect to a minimum number of local
models and guaranteed global performance. Furthermore, the
confidence and prediction intervals of the identified model are
supplied. Simulation results demonstrate the performance of the
proposed criterion.

Wiener model, local linear models, least squares, prediction error method.