Matrix analysis
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
On adaptive stabilization of time-varying stochastic systems
SIAM Journal on Control and Optimization
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)
Identification of Hammerstein Nonlinear Stochastic Systems
Automation and Remote Control
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
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Identification of nonlinear stochastic systems in the class of Hammerstein–Wiener models is studied. The problem is specific due to the nonlinearities of the investigated object. Hammerstein–Wiener models are constructed with regard for the disturbances of the type of white noise and martingale sequence at the output of the object. A two-stage recursive identification algorithm is designed. Necessary and sufficient conditions for the strong consistency of parameter estimates found by this algorithm are formulated. The results are applied to adaptive tracking of the output of an object.