Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Bandpass nonlinear systems identification by higher order crosscorrelation
IEEE Transactions on Signal Processing
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models
Automatica (Journal of IFAC)
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
Decoupling the linear and nonlinear parts in Hammerstein model identification
Automatica (Journal of IFAC)
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
Bounded error identification of Hammerstein systems through sparse polynomial optimization
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper analyzes the computational complexity of set membership identification of Hammerstein and Wiener systems. Its main results show that, even in cases where a portion of the plant is known, the problems are generically NP-hard both in the number of experimental data points and in the number of inputs (Wiener) or outputs (Hammerstein) of the nonlinearity. These results provide new insight into the reasons underlying the high computational complexity of several recently proposed algorithms and point out the need for developing computationally tractable relaxations.