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Neural Computing and Applications

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01.09.2004 | Original Paper

Fuzzy control of an ANFIS model representing a nonlinear liquid-level system

verfasst von: S. N. Engin, J. Kuvulmaz, V. E. Ömurlü

Erschienen in: Neural Computing and Applications | Ausgabe 3/2004

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Abstract

This paper aims to serve two main objectives; one is to demonstrate the modelling capabilities of a neuro-fuzzy approach, namely ANFIS (adaptive-network based fuzzy inference system) to a nonlinear system; and the other is to design a fuzzy controller to control such a system. The nonlinear system, which is a liquid-level system, is represented first by its mathematical model and then by ANFIS architecture. The ANFIS model is formed by means of input–output data set taken from the mathematical model. Then a PID-type fuzzy controller, which linguistically approximates the classical three-term compensation, was designed to control the system represented by both its mathematical and ANFIS models in order to perform an agreement comparison between them. It is shown that the ANFIS architecture can model a nonlinear system very accurately by means of input–output pairs obtained either from the actual system or its mathematical model. It is also shown that such a system can be controlled effectively by a fuzzy controller.

Vorheriger Artikel A review of genetic algorithms applied to training radial basis function networks

Nächster Artikel Discrete time neuro sliding mode control with a task-specific output error

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Titel: Fuzzy control of an ANFIS model representing a nonlinear liquid-level system
verfasst von: S. N. Engin
J. Kuvulmaz
V. E. Ömurlü
Publikationsdatum: 01.09.2004
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2004
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-004-0405-4

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