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This paper considers the identification problem of the ARMA system followed by a binary sensor, in which the internal variables are corrupted by additive ARMA noises. Recursive estimates for the parameters of the linear system and for the threshold of the binary sensor are given by the stochastic approximation algorithms with expanding truncations...
This paper concerns the identification problem of errors-in-variables (EIV) systems with nonlinear output observations. Under independent and identically distributed (iid) Gaussian inputs with unknown variance, recursive algorithms for estimating the parameters of the EIV systems are presented. For a large class of nonlinear observations, condition...
This paper considers identification of the nonlinear autoregression with exogenous inputs (NARX system). The growth rate of the nonlinear function is required be not faster than linear with slope less than one. The value of f(·) at any fixed point is recursively estimated by the stochastic approximation (SA) algorithm with the help of kernel functi...
This paper considers identification of Wiener systems for which the internal variables and output are corrupted by noises. When the internal noise is a sequence of independent and identically distributed (iid) Gaussian random variables, by the Weierstrass transformation (WT) the system under consideration turns to be a Wiener system without interna...