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.