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C. Hametner, S. Jakubek:
"Nonlinear Dynamic MISO Identification in a Complex Noisy Environment";
Talk: Chinese Control Conference, Kunming, Yunnan, China; 2008-07-16 - 2008-07-18; in: "Proceedings of the 27th Chinese Control Conference", (2008), ISBN: 9787900719706; 6 pages.

An algorithm for nonlinear dynamic system identification using the Generalised Total Least Squares parameter estimation
algorithm is presented in this paper. In many practical applications noise in measured input channels results in parameter
estimates which are not consistent when conventional Least Squares parameter estimation methods are used. Total Least Squares
methodologies are limited to situations where all inputs and the output respectively are corrupted by noise. A proper extension
is the Generalised Total Least Squares algorithm which is able to cope with situations where some input channels of a MISO
system are noise-free while others are taken from measurements and are thus subject to noise. The GTLS algorithm together
with a well-tried model construction algorithm which is based on an hierarchical logistic discriminant tree results in an efficient
algorithm for nonlinear dynamic identification.

Neuro-fuzzy modelling, Logistic discriminant tree, Generalised total least squares, Errors-in-variables model