The identification of nonlinear biological systems: LNL cascade models
Biological Cybernetics
The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Theory of linear and integer programming
Theory of linear and integer programming
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Controller design oriented model identification method for Hammerstein system
Automatica (Journal of IFAC)
Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
Harmonic signal modeling using adaptive nonlinear function estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Recursive subspace identification of linear and non-linear Wiener state-space models
Automatica (Journal of IFAC)
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
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In this paper a three stage procedure is presented for deriving parameters bounds of SISO Wiener models when the nonlinear block is modeled by a possibly noninvertible polynomial and the output measurement errors are bounded. First, using steady-state input-output data, parameters of the nonlinear part are bounded by a tight orthotope. Then, given the estimated uncertain nonlinearity and the output measurements collected exciting the system with an input dynamic signal, bounds on the unmeasurable inner signal are computed. Finally, such bounds, together with noisy output measurements, are used for bounding the parameters of the linear block.