Comprehensive modeling of U-tube steam generators using extreme learning machines

BEYHAN S., Kavaklioglu K.

IEEE Transactions on Nuclear Science, cilt.62, sa.5, ss.2245-2254, 2015 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 62 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/tns.2015.2462126
  • Dergi Adı: IEEE Transactions on Nuclear Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2245-2254
  • Anahtar Kelimeler: 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)
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

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.