Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

C. Hametner, S. Jakubek:
"Optimisation Of Relative Error Criteria In Nonlinear Neuro-Fuzzy Identification";
Vortrag: 20th IASTED International Conference "Modelling and Simulation", Banff; 06.07.2009 - 08.07.2009; in: "Proceedings of the 20th IASTED International Conference "Modelling and Simulation"", (2009), ISBN: 978-0-88986-799-4; Paper-Nr. 670-071, 6 S.

Kurzfassung englisch:
In this paper an approach for the minimisation of user defined
performance criteria in nonlinear Neuro-Fuzzy identification
is presented. Neuro-Fuzzy models are an effective
means to partition nonlinear functions into subdomains
which are then described by local regressionmodels.
In many practical applications varying noise in measured
data is an important problem both for regression model
parametrisation and partitioning based on available data.
As a solution approach the proposed algorithm allows for
the incorporation of relative performance criteria to achieve
a desired relative accuracy with a small number of local
models. The main advantage of the proposed algorithm is
that relative weights are not only used for the computation
of the local model parameters but also for the determination
of the region of validity of the local models. Using the
proposed algorithm the optimisation of the partitions is focused
on the regions of interest of the input space regarding
a relative (local) performance criterion. The effectiveness
of the proposed concepts is demonstrated by means of an
illustrative and an application example.

Neuro-Fuzzy modelling, relative error, optimisation, nonlinear systems, parameter estimation

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.