State of the Art in Nonlinear Dynamical System Identification using Artificial Neural Networks

Author

Todorovic, Nenad ; Klan, Petr

Author_Institution

Dept. of Instrum. & Control Eng., Czech Tech. Univ., Prague

fYear

2006

fDate

25-27 Sept. 2006

Firstpage

103

Lastpage

108

Abstract

This paper covers the state of the art in nonlinear dynamical system identification using artificial neural networks (ANN). The main approaches in the last two decades are presented in unified framework. ANN has unique characteristics, which enable them to model nonlinear dynamical systems. The main problems with the choice of ANN model structure are considered and commonly used identification schemes are proposed. A procedure for derivation of parameter estimation law using Lyapunov synthesis approach, which guarantees stability and convergence of the overall identification scheme, is presented

Keywords

Lyapunov methods; neural nets; nonlinear dynamical systems; parameter estimation; Lyapunov synthesis approach; artificial neural networks; convergence; nonlinear dynamical system identification; parameter estimation law; stability; Artificial neural networks; Frequency; Neurons; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Seminars; Stability; System identification; White noise; Artificial Neural Networks; Nonlinear Dynamical Systems; Nonlinear Identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on

Conference_Location

Belgrade, Serbia & Montenegro

Print_ISBN

1-4244-0433-9

Electronic_ISBN

1-4244-0433-9

Type

conf

DOI

10.1109/NEUREL.2006.341187

Filename

4147175