DocumentCode
490560
Title
A New Method for Identifying Orders of Input-Output Models for Nonlinear Dynamic Systems
Author
He, Xiangdong ; Asada, Haruhiko
Author_Institution
Center for Information-Driven Mechanical Systems, Department of Mechanical Engineering, Massachusetts Institute of Technology
fYear
1993
fDate
2-4 June 1993
Firstpage
2520
Lastpage
2523
Abstract
The paper proposes a new method for identifying orders of input-output models for unknown nonlinear dynamic systems. The approach is based on the continuity property of the nonlinear functions which represent input-output models of continuous dynamic systems. The approach does not depend on any nonlinear function approximation methods and solely depends on the system´s input-output data measured in experiments. By evaluating the modification of an index which is defined as Lipschitz number with the successive modification of model orders, the appropriate model orders can be determined simply and reliably. Theoretical background of the present approach is discussed. Several examples from chaotic dynamic systems, nonlinear plant models are presented to demonstrate the effectiveness of the present method.
Keywords
Approximation methods; Artificial neural networks; Function approximation; Helium; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Polynomials; Predictive models; Radial basis function networks;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1993
Conference_Location
San Francisco, CA, USA
Print_ISBN
0-7803-0860-3
Type
conf
Filename
4793346
Link To Document