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Hametner, C., & Jakubek, S. (2007). Neuro-Fuzzy Modelling Using a Logistic Discriminant Tree. In Proceedings of the 2007 American Control Conference (p. 6). http://hdl.handle.net/20.500.12708/65564
E325-04 - Forschungsbereich Regelungstechnik und Prozessautomatisierung E325-04-2 - Forschungsgruppe Regelungsmethoden-Antriebssysteme
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Published in:
Proceedings of the 2007 American Control Conference
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Date (published):
2007
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Event name:
American Control Conference 2007
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Event date:
11-Jul-2007 - 13-Jul-2007
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Event place:
New York City, USA, Non-EU
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Number of Pages:
6
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Peer reviewed:
Yes
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Keywords:
Takagi-Sugeno Fuzzy Models; nonlinear system identification; discriminant tree; Expectation-Maximisation
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Abstract:
An algorithm for nonlinear static and dynamic
identification using Takagi-Sugeno Fuzzy Models is presented.
For practical applications the incorporation of prior knowledge
and the interpretability of the local models is of great interest.
Using a tree structured algorithm in combination with the
distinction between the input arguments for the consequents and
for the premises the nonlinear optimisation is performed in an
efficient way. The axis oblique decomposition of the partition
space is based on an Expectation-Maximisation (EM) algorithm.
Simulation results demonstrate the capabilities of the proposed
concept.
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Research Areas:
Modelling and Simulation: 70% Computational System Design: 30%
