The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Nonlinear black-box models in system identification: mathematical foundations
Automatica (Journal of IFAC) - Special issue on trends in 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)
Identification of Wiener systems with binary-valued output observations
Automatica (Journal of IFAC)
Space and time complexities and sensor threshold selection in quantized identification
Automatica (Journal of IFAC)
Quantifying the accuracy of Hammerstein model estimation
Automatica (Journal of IFAC)
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Space and time complexities and sensor threshold selection in quantized identification
Automatica (Journal of IFAC)
Brief paper: An efficient sensor quantization algorithm for decentralized estimation fusion
Automatica (Journal of IFAC)
Recursive projection algorithm on FIR system identification with binary-valued observations
Automatica (Journal of IFAC)
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This work is concerned with identification of Hammerstein systems whose outputs are measured by quantized sensors. The system consists of a memoryless nonlinearity that is polynomial and possibly noninvertible, followed by a linear subsystem. The parameters of linear and nonlinear parts are unknown but have known orders. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the quantization thresholds are known. The concept of strongly scaled full rank signals is introduced to capture the essential conditions under which the Hammerstein system can be identified with set-valued observations. Under strongly scaled full rank conditions, a strongly convergent algorithm is constructed. Asymptotic consistency and efficiency of the algorithm are investigated.