Nonlinear system modelling: How to estimate the highest significant order

Conference Record - IEEE Instrumentation and Measurement Technology Conference

Volumn 1, Issue , 2002, Pages 353-358

  Chiras, Neophytos ^a   Evans, Ceri ^a   Rees, David ^a   Solomou, Michael ^a  

^a UNIVERSITY OF GLAMORGAN   (United Kingdom)

Author keywords

Frequency domain; Frequency response; Multisine signals; NARMAX models; Nonlinear systems; Order of nonlinearity; Volterra series

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONTROL NONLINEARITIES; ESTIMATION; FREQUENCY RESPONSE; MATHEMATICAL MODELS; SYSTEMS ANALYSIS;

FREQUENCY DOMAIN METHOD; HIGHEST SIGNIFICANT ORDER; MULTISINE SIGNALS; VOLTERRA SERIES;

NONLINEAR SYSTEMS;

EID: 0036049699     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. The interpretation of kernels - an overview
2. Measuring Volterra kernels (II)
3. Measurement of the Wiener kernels of a non-linear system by cross-correlation
4. Measurement of second-order Volterra kernels using pseudorandom ternary signals
5. Measuring Volterra kernels III: How to estimate the highest significant order
6. Factors affecting Volterra kernel estimation: Emphasis on lung tissue viscoelasticity
7. Representations of non-linear systems: The NARMAX model
8. Input-output parametric models for non-linear systems. Part II: Stochastic non-linear systems
9. Representations of non-linear systems: The NARMAX model
10. Identification of nonlinear systems: The NARMAX polynomial model approach
11. Identification of aircraft gas turbine dynamics using frequency-domain techniques
12. Identification of multi-input multi-output systems using frequency-domain methods
13. Multivariable modeling of gas turbine dynamics
14. Frequency response functions measurements in the presence of nonlinear distortions