CONTINUOUS-TIME WIENER SYSTEM-IDENTIFICATION

A continuous-time Wiener system is identified. The system consists of a linear dynamic subsystem and a memoryless nonlinear one connected in a cascade. The input signal is a stationary white Gaussian random pro cess. The system is disturbed by stationary white random Gaussian nois e. Both subsystems are identified from input-output observations taken at the input and output of the whole system. The a priori information is very small and, therefore, resulting identification problems are n onparametric. The impulse response of the linear Dart is recovered by a correlation method, while the nonlinear characteristic is estimated with the help of the nonparametric kernel regression method. The autho rs prove convergence of the proposed identification algorithms and exa mine their convergence rates.