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Z. Nagy, S. Agachi and L. Bodizs, “Adaptive Neural Network Model Based Nonlinear Predictive Control of a Fluid Catalytic Cracking Unit,” Computer Aided Chemical Engineering, Vol. 8, 2000, pp. 235-240. doi:10.1016/S1570-7946(00)80041-3

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.