D. G. Stavrakoudis and J. B. Theocharis, “Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction,” IEEE Transactions on Systems, Man, and Cybernetics, (Part B): Cybernetics, Vol. 37, No. 5, 2007, pp. 1305-1320. doi:10.1109/TSMCB.2007.900516

has been cited by the following article:

• TITLE: Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

  AUTHORS: Farouk Zouari, Kamel Ben Saad, Mohamed Benrejeb

  KEYWORDS: Complex Dynamical Systems; Lyapunov Approach; Recurrent Neural Networks; Adaptive Control

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.4, April 23, 2012

  ABSTRACT: In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.