The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
System identification: theory for the user
System identification: theory for the user
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Automatica (Journal of IFAC)
Time series: data analysis and theory
Time series: data analysis and theory
Blind identification of LTI-ZMNL-LTI nonlinear channel models
IEEE Transactions on Signal Processing
Blind identification of Volterra-Hammerstein systems
IEEE Transactions on Signal Processing - Part I
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Theoretical analysis and performance of OFDM signals in nonlinear fading channels
IEEE Transactions on Wireless Communications
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Brief Fast approximate identification of nonlinear systems
Automatica (Journal of IFAC)
Box-Jenkins identification revisited-Part I: Theory
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
Identification of Hammerstein-Wiener models
Automatica (Journal of IFAC)
Consistent identification of Wiener systems: A machine learning viewpoint
Automatica (Journal of IFAC)
Hi-index | 35.69 |
This paper is about the identification of discrete-time Wiener systems from output measurements only (blind identification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum-likelihood estimator is constructed. Its asymptotic properties are analyzed and the Cramér-Rao lower bound is calculated. A two-step procedure for generating high-quality initial estimates is presented as well. The paper includes the Illustration of the method on a Simulation example.