Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6057
Title: A Comparison of ANFIS, MLP and SVM in Identification of Chemical Processes
Authors: Efe, Mehmet Önder
Keywords: [No Keywords]
Issue Date: 2009
Publisher: IEEE
Source: IEEE International Conference on Control Applications/International Symposium on Intelligent Control -- JUL 08-10, 2009 -- St Petersburg, RUSSIA
Series/Report no.: IEEE International Conference on Control Applications
Abstract: This paper presents a comparison of Adaptive Neuro Fuzzy Inference Systems (ANFIS), Multi layer Perceptron (MLP) and Support Vector Machines (SVMs) in identification of a chemical process displaying a rich set of dynamical responses under different operating conditions. The methods considered are selected carefully as they are the foremost approaches exploiting the linguistic representations in ANFIS, connectionist representations in MLP and machine learning based on structural risk minimization in SVM. The comparison metrics are the computational complexity measured by the propagation delay, realization performance and design simplicity. It is seen that SVM algorithm performs better in terms of providing an accurate fit to the desired dynamics but a very close performance result can also be obtained with ANFIS with significantly lower computational cost. Performance with MLP is comparably lower that the other two algorithms yet MLP structure has the lowest computational complexity.
URI: https://doi.org/10.1109/CCA.2009.5281184
https://hdl.handle.net/20.500.11851/6057
ISBN: 978-1-4244-4601-8
ISSN: 1085-1992
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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