Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/10017
Title: Comprehensive modeling of U-tube steam generators using extreme learning machines
Authors: Beyhan, Selami
Kavaklıoğlu, Kadir
Keywords: Extreme learning machine
fuzzy system
minimum-descriptive-length (MDL)
neural-network
online and offline identification
root-mean-squared error (RMSE)
U-tube steam generator (UTSG)
Backpropagation
Fuzzy inference
Fuzzy logic
Fuzzy systems
Knowledge acquisition
Learning systems
Mean square error
Neural networks
Water levels
Backpropagation learning
Comprehensive model
Model performance
Non-linear autoregressive with exogenous
Quantitative comparison
Root mean squared errors
U-tube steam generators
Steam generators
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed. © 1963-2012 IEEE.
URI: https://hdl.handle.net/11499/10017
https://doi.org/10.1109/TNS.2015.2462126
ISSN: 0018-9499
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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