Discrete-time recurrent high order neural networks for nonlinear identification

This paper focuses on the problem of discrete-time nonlinear system identification via recurrent high order neural networks. It includes the respective stability analysis on the basis of the Lyapunov approach for the NN training algorithm. Applicability of the proposed scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

[1]  Prashanth Krishnamurthy,et al.  Modeling and Adaptive Nonlinear Control of Electric Motors , 2003 .

[2]  Alexander G. Loukianov,et al.  Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks , 2007, IEEE Transactions on Neural Networks.

[3]  Joydeep Ghosh,et al.  Efficient Higher-Order Neural Networks for Classification and Function Approximation , 1992, Int. J. Neural Syst..

[4]  Manolis A. Christodoulou,et al.  Adaptive Control with Recurrent High-order Neural Networks , 2000, Advances in Industrial Control.

[5]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[6]  L. J. Ricalde,et al.  Trajectory tracking via adaptive recurrent control with input saturation , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[7]  Abir Jaafar Hussain,et al.  Higher order neural networks and their applications to financial time series prediction , 2006, Artificial Intelligence and Soft Computing.

[8]  Vadim I. Utkin,et al.  Sliding mode control in electromechanical systems , 1999 .

[9]  Frank L. Lewis,et al.  Adaptive Approximation Based Control-Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches-[Book review; J. A. Farrell and M. M. Polycarpou] , 2007 .

[10]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[11]  Wen Yu,et al.  Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms , 2004, Inf. Sci..

[12]  Jin Zhang,et al.  Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Alexander S. Poznyak,et al.  Differential Neural Networks for Robust Nonlinear Control , 2004, IEEE Transactions on Neural Networks.

[14]  Jorge Rivera,et al.  DISCRETE-TIME SLIDING MODE CONTROL OF AN INDUCTION MOTOR , 2002 .

[15]  Jonas Sjöberg,et al.  Neural networks for modelling and control of dynamic systems, M. Nørgaard, O. Ravn, N. K. Poulsen and L. K. Hansen, Springer, London, 2000, xiv+246pp. , 2001 .

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Vadim I. Utkin,et al.  Sliding mode control , 2004 .