The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Estimation of quantized linear errors-in-variables models
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
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)
Asymptotically efficient parameter estimation using quantized output observations
Automatica (Journal of IFAC)
Stochastic gradient identification of polynomial Wiener systems: analysis and application
IEEE Transactions on Signal Processing
Adaptive filtering using quantized output measurements
IEEE Transactions on Signal Processing
Quantifying the accuracy of Hammerstein model estimation
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief Fast approximate identification of nonlinear systems
Automatica (Journal of IFAC)
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
Nonlinear system identification via direct weight optimization
Automatica (Journal of IFAC)
Δ-Entropy: Definition, properties and applications in system identification with quantized data
Information Sciences: an International Journal
Identification of Hammerstein Systems with Quantized Observations
SIAM Journal on Control and Optimization
On identification of FIR systems having quantized output data
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This work is concerned with identification of Wiener systems whose outputs are measured by binary-valued sensors. The system consists of a linear FIR (finite impulse response) subsystem of known order, followed by a nonlinear function with a known parametrization structure. The parameters of both linear and nonlinear parts are unknown. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the nonlinearity is continuous and invertible. It is shown that under scaled periodic inputs, identification of Wiener systems can be decomposed into a finite number of core identification problems. The concept of joint identifiability of the core problem is introduced to capture the essential conditions under which the Wiener system can be identified with binary-valued observations. Under scaled full-rank conditions and joint identifiability, a strongly convergent algorithm is constructed. The algorithm is shown to be asymptotically efficient for the core identification problem, hence achieving asymptotic optimality in its convergence rate. For computational simplicity, recursive algorithms are also developed.