Identification of Volterra kernels of non-linear systems

This paper presents an identification method for non-linear systems whose the input-output functional is regular and homogeneous. The model is a Volterra series truncated in its first terms.

[1]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[2]  P. Coirault,et al.  Continuous-Time System Identification by Overparametrized Model and Orthonormal Functions , 1997 .

[3]  Hiroshi Kashiwagi,et al.  Identification of Volterra Kernels of Nonlinear Systems by use of Μ-Sequence Correlation , 1997 .

[4]  S. Billings,et al.  Identification of non-linear systems using correlation analysis and pseudorandom inputs , 1980 .

[5]  B. Ninness,et al.  A unifying construction of orthonormal bases for system identification , 1997, IEEE Trans. Autom. Control..

[6]  David Rees,et al.  Probing signals for measuring nonlinear Volterra kernels , 1995, Proceedings of 1995 IEEE Instrumentation and Measurement Technology Conference - IMTC '95.

[7]  J. J. O'Reilly,et al.  Experimental validation of Volterra series nonlinear modelling for microwave subcarrier optical systems , 1996 .

[8]  E. Zafiriou,et al.  Nonlinear dynamical system identification using reduced Volterra models with generalised orthonormal basis functions , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[9]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[10]  Boualem Boashash,et al.  Identification of a class of nonlinear systems under stationary non-Gaussian excitation , 1997, IEEE Trans. Signal Process..

[11]  W. Frank Sampling requirements for Volterra system identification , 1996, IEEE Signal Processing Letters.

[12]  N. Sreedhar Non-asymptotic stability of complex valued differential systems† , 1970 .