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R. Zhan and J. Wan “Neural Network-Aided Adaptive Unscented Kalman Filter for Nonlinear State Estimation,” IEEE Signal Processing Letters, Vol. 13, No. 7, 2006, pp. 445-448. doi:10.1109/LSP.2006.871854

has been cited by the following article:

• TITLE: Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network

  AUTHORS: Tarek Aboueldahab, Mahumod Fakhreldin

  KEYWORDS: Sigmoid Diagonal Recurrent Neural Networks, Dynamic Back Propagation, Dynamic Nonlinear Systems, Adaptive Control

  JOURNAL NAME: Intelligent Control and Automation, Vol.2 No.3, August 8, 2011

  ABSTRACT: The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.