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

Şen G. D., Gunel G. O., Güzelkaya M.

2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020, İstanbul, Türkiye, 15 - 17 Ekim 2020 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/asyu50717.2020.9259812
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2020 IEEE.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.