The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
Brief paper: Recursive identification for multivariate errors-in-variables systems
Automatica (Journal of IFAC)
Identifiability of errors in variables dynamic systems
Automatica (Journal of IFAC)
Maximum likelihood identification of Wiener models
Automatica (Journal of IFAC)
New Method of Order Estimation for ARMA/ARMAX Processes
SIAM Journal on Control and Optimization
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
This paper considers the recursive identification of errors-in-variables (EIV) Wiener systems composed of a linear dynamic system followed by a static nonlinearity. Both the system input and output are observed with additive noises being ARMA processes with unknown coefficients. By a stochastic approximation incorporated with the deconvolution kernel functions, the recursive algorithms are proposed for estimating the coefficients of the linear subsystem and for the values of the nonlinear function. All the estimates are proved to converge to the true values with probability one. A simulation example is given to verify the theoretical analysis.