Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6062
Title: A comparison of networked approximators in parallel mode identification of a bioreactor
Authors: Efe, Mehmet Önder
Keywords: Bioreactor
Identification
Multilayer perceptron
ANFIS
Support vector machine
Chemical process modeling
Issue Date: 2010
Publisher: Elsevier Sci Ltd
Abstract: This paper presents a simulation based comparison of Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Least Squares Support Vector Machines (LS-SVM) in parallel mode identification of a chemical process displaying several challenges. The paper provides a graphical analysis of the nonlinear behavior for the system under investigation, a case study of purely parallel identification scheme, the effects of noise in the training data on the prediction performance and the performance comparison of the standard approaches under limited amount of numerical data. The results have shown that the emulators utilizing the MLP structure are superior to the others in terms of predicting the system trajectories, locating the limit cycle, noise driven response and predicting the steady state conditions given only 582 pairs of training data. Furthermore, as opposed to others, with the MLP structure, these qualities disappear smoothly as the noise level is increased gradually. (C) 2010 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.advengsoft.2010.07.004
https://hdl.handle.net/20.500.11851/6062
ISSN: 0965-9978
1873-5339
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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