Extended Kalman Filter Based Modified Elman-Jordan Neural Network for Control and Identification of Nonlinear Systems

Gokcen Devlet Sen, Gulay Oke Gunel, Mujde Guzelkaya

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

8 Atıf (Scopus)

Özet

In this paper, the Extended Kalman Filter (EKF) is used for online training of a recurrent neural network (RNN) model since the EKF outperforms the first order gradient-based algorithms as a second order method. The modified Elman-Jordan Neural Network model with one hidden layer is adopted as the RNN structure. Self-connections are added in context units to investigate their effects. Then, this model is utilized for identification and online control of a nonlinear single input single output (SISO) process model. The performance of the proposed structure is evaluated by simulation results. The effects of some parameters and the number of hidden units to the performance are also examined.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728191362
DOI'lar
Yayın durumuYayınlandı - 15 Eki 2020
Etkinlik2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 - Istanbul, Turkey
Süre: 15 Eki 202017 Eki 2020

Yayın serisi

AdıProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020

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???event.eventtypes.event.conference???2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot15/10/2017/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

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